BEGIN:VCALENDAR VERSION:2.0 PRODID:-//POPNET - ECPv6.15.20//NONSGML v1.0//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALNAME:POPNET X-ORIGINAL-URL:https://www.popnet.io X-WR-CALDESC:Events for POPNET REFRESH-INTERVAL;VALUE=DURATION:PT1H X-Robots-Tag:noindex X-PUBLISHED-TTL:PT1H BEGIN:VTIMEZONE TZID:Europe/Amsterdam BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20210328T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20211031T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20220327T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20221030T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20230326T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20231029T010000 END:STANDARD BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20240331T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20241027T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230629T213000 DTEND;TZID=Europe/Amsterdam:20230629T225000 DTSTAMP:20260503T162457 CREATED:20230512T123603Z LAST-MODIFIED:20230512T123707Z UID:1095-1688074200-1688079000@www.popnet.io SUMMARY:Node anonymity in networks: The infectiousness of uniqueness DESCRIPTION:On Thursday 29 June\, POPNET PhD candidate Rachel de Jong will present her work on “Node anonymity in networks: The infectiousness of uniqueness” at Sunbelt 2023\, which will take place in Portland\, Oregon. URL:https://www.popnet.io/events/node-anonymity-in-networks-the-infectiousness-of-uniqueness/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/05/insna-sunbelt-portland-or-2023-525x225-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230629T184000 DTEND;TZID=Europe/Amsterdam:20230629T200000 DTSTAMP:20260503T162457 CREATED:20230512T123924Z LAST-MODIFIED:20230512T123925Z UID:1097-1688064000-1688068800@www.popnet.io SUMMARY:Social-economic segregation in a Population-Scale Social Network DESCRIPTION:On Thursday 29 June\, POPNET PhD candidate Yuliia Kazmina will present her work on “Social-economic segregation in a Population-Scale Social Network” at Sunbelt 2023\, which will take place in Portland\, Oregon. URL:https://www.popnet.io/events/social-economic-segregation-in-a-population-scale-social-network/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/05/insna-sunbelt-portland-or-2023-525x225-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230629T184000 DTEND;TZID=Europe/Amsterdam:20230629T200000 DTSTAMP:20260503T162457 CREATED:20230512T123924Z LAST-MODIFIED:20230512T124244Z UID:1099-1688064000-1688068800@www.popnet.io SUMMARY:The small-world structure of a population-scale social network DESCRIPTION:On Thursday 29 June\, POPNET PI Frank Takes will present his work on “The small-world structure of a population-scale social network” at Sunbelt 2023\, which will take place in Portland\, Oregon. URL:https://www.popnet.io/events/the-small-world-structure-of-a-population-scale-social-network-2/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/05/insna-sunbelt-portland-or-2023-525x225-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230629T184000 DTEND;TZID=Europe/Amsterdam:20230629T200000 DTSTAMP:20260503T162457 CREATED:20230512T123227Z LAST-MODIFIED:20230512T123303Z UID:1091-1688064000-1688068800@www.popnet.io SUMMARY:The anatomy of a population-scale social network DESCRIPTION:On Thursday 29 June\, POPNET Postdoctoral Researcher Eszter Bokányi will present her work on “The anatomy of a population-scale social network” at Sunbelt 2023\, which will take place in Portland\, Oregon. URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network-5/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/05/insna-sunbelt-portland-or-2023-525x225-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230522T110000 DTEND;TZID=Europe/Amsterdam:20230522T120000 DTSTAMP:20260503T162457 CREATED:20230508T112232Z LAST-MODIFIED:20230516T120449Z UID:1080-1684753200-1684756800@www.popnet.io SUMMARY:POPNET Connects with Bas Hofstra DESCRIPTION:This seminar is hybrid. Please register to attend the seminar online or on-site via the button. You will receive a link to the virtual meeting via email or a confirmation containing the on-site location \n\n\n\n\n\nAcademic Migration: Interdisciplinary Hierarchy\, Closure\, or Similarity?\n\n\n\nThe last two decades have seen a surge in research initiatives in many scientific fields surrounding interdisciplinarity. This has spawned many interdisciplinary research centers on US university campuses supported by billions of raised university or federal grant money to educate students as well as to facilitate interdisciplinary exchange and collaborations between faculty. This is often led by the belief that interdisciplinary exchange in science pushes research fields forward and accelerates breakthrough discovery. Interdisciplinary scientific collaboration is argued to pull together diverse insights from multiple bodies of knowledge\, is unrestricted by disciplinary boundaries or semantics\, and can draw from a larger methodological tool set. \n\n\n\nYet in spite of the push for interdisciplinary exchanges\, there is surprisingly little empirical knowledge on its prevalence\, patterning\, or determinants. This is remarkable\, because prior work shows that the social fabric of science itself is inherently shaped by exchanges of ideas\, knowledge\, and scholars themselves. Here\, we focus on the latter and study scholarly hiring between disciplines. How prevalent are such exchanges between disciplines and how did this develop over time? And\, more importantly\, what determines the emergence of these interdisciplinary exchange structures: do some disciplines disproportionately place faculty in other disciplines\, do disciplines cluster in such a hiring network\, or are interdisciplinary hires mostly explained by how intellectually similar discipline are? These questions motivate the main goals of this study: identifying patterns of interdisciplinary exchange and explaining the emergence and persistence of these network structures. \n\n\n\nWe build on and extend prior work\, particular the branches of literature on interdepartmental faculty hiring. Hiring often involves exchange indicative of implicit judgment; when one department hires a graduate student of another as faculty\, there is a positive assessment of the graduate department that places the student. The assortment of these dyadic exchanges across disciplines represent migration networks of scholars that illuminate disciplinary hierarchy\, clustering\, and similarity. Our study empirically considers these network dynamics\, thus providing insight into which disciplines wield the most influence in knowledge and scholarly exchange and why. \n\n\n\nOur empirical site contains a realized scientific migration market of approximately 1.03 million records of nearly all US PhD students and corresponding metadata – names\, supervisors\, disciplines\, and so forth – from their PhD theses (1980-2010). These data capture a wide cross-section of scholarly disciplines (N = 51) PhD-granting universities (N = 221)\, and departments (N = 8\,205). What is particularly useful about this database is that it allows us to follow PhD recipients through time in a near-closed system of PhD recipients and their scholarly careers moving onward\, thus showing the interdisciplinarization – which disciplines place students where and why? – of US academia. We analyze these interdisciplinary exchanges through a series of stERGMs that include nodal (discipline size\, popularity)\, dyadic (natural language processing measures of intellectual distance between disciplines\, field homophily)\, and closure dynamics. \n\n\n\n\n\n\n\nAbout Bas Hofstra\n\n\n\nBas Hofstra is Assistant Professor at Radboud University’s Department of Sociology. His work orbits the study of diversity\, stratification\, and innovation. It captures longitudinal systems of social and cultural exchange: from the gestation and birth of networks\, careers\, ideas\, or innovations\, to their use\, up until their eventual cessation. As such\, his work strives for three interrelated goals: (i) answering substantive questions on causes and effects of social networks\, while (ii) contributing to social theory\, and (iii) using computational methods and big data. His research appeared (among others) in PNAS\, American Sociological Review\, Social Forces\, Social Networks\, and Nature Human Behaviour\, and was honored with several grants and awards. URL:https://www.popnet.io/events/popnet-connects-with-bas-hofstra/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/05/POPNET-Connects-with-Bas-Hofstra.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230516T100000 DTEND;TZID=Europe/Amsterdam:20230516T110000 DTSTAMP:20260503T162457 CREATED:20230328T133438Z LAST-MODIFIED:20230330T075655Z UID:1063-1684231200-1684234800@www.popnet.io SUMMARY:Segregation in population scale social networks DESCRIPTION:Lecture by Eelke Heemskerk and Yuliia Kazmina at the Sociology Department of Utrecht University \n\n\n\nWe propose a social network-aware approach to study socio-economic segregation. The key question is whether patterns of segregation are more pronounced in social networks than the common spatial manifestations of segregation. We conduct a population-scale social network analysis to uncover socio-economic segregation at a comprehensive and highly granular level. At the basis of this analysis is high quality register data consisting of complete information on $\sim$17.2 million registered residents of the Netherlands that are connected through 1.3 billion ties distributed over four distinct tie types. By comparing income assortativity between the social network and the spatial perspective\, we find that the social network structure exhibits  a factor of two higher segregation.  This may signal  that while at a particular  scale of spatial aggregation (e.g.\, the geographical  neighborhood)\, patterns of socio-economic segregation appear to be minimal\,  they in fact persist in the underlying social network structure. Furthermore\, we discover higher socioeconomic segregation in larger cities as opposed to a widespread view of cities as hubs for diverse socioeconomic mixing. A population scale  social network perspective hence offers a way to uncover hitherto “hidden” segregation that extends beyond spatial neighborhoods and infiltrates multiple aspects of human life. URL:https://www.popnet.io/events/segregation-in-population-scale-social-networks/ CATEGORIES:Lecture ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2023/03/Smaller-logo-page-Ultecht-University.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230508T100000 DTEND;TZID=Europe/Amsterdam:20230508T110000 DTSTAMP:20260503T162457 CREATED:20230501T095640Z LAST-MODIFIED:20230501T101407Z UID:1073-1683540000-1683543600@www.popnet.io SUMMARY:POPNET Connects with Miranda Lubbers and Michał Bojanowski DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\n\n \n\n\n\nSimulating society-wide networks based on NSUM\n\n\n\nIn a quest to understand larger patterns of sociability in a society and in particular its cohesion\, different methods have been employed based on survey data\, register data\, and social media data. In this talk\, we will discuss the ERC Advanced Grant project PATCHWORK\, which intends to simulate society-wide networks based on the Network Scale-Up Method (NSUM) that will be implemented in a cross-national survey. We will discuss its unique features\, how it compares to other methods used so far\, and potential benefits of integrating different methods of simulating society-wide methods. \n\n\n\n\n\n\n\n\nAbout Miranda Lubbers\n\n\n\nMiranda Lubbers (PhD from Groningen University\, the Netherlands) is Professor in Social and Cultural Anthropology of the Autonomous University of Barcelona (UAB)\, Spain\, director of the COALESCE Lab\, and an ICREA Acadèmia fellow. Her research studies how social networks shape processes of social cohesion\, polarization\, and exclusion. \n\n\n\n\n\n\n\n\n\nAbout Michał Bojanowski\n\n\n\nMichał Bojanowski is a computational sociologist (PhD from Utrecht University\, the Netherlands)\, researcher and Assistant Professor\, and R developer and trainer. His main research interests focus on (the dynamics of) social networks and mathematical/computational social science as tools for understanding conflict and cooperation. He is a researcher at the COALESCE Lab\, Universitat Autònoma de Barcelona\, participating in the Patchwork project\, and an Assistant professor at the Chair of Quantitative Methods and Information Technology\, Kozminski University. URL:https://www.popnet.io/events/popnet-connects-with-miranda-lubbers-and-michal-bojanowski/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/05/POPNET-Connects-with-Miranda-Lubbers-and-Michal-Bojanowski.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230220T100000 DTEND;TZID=Europe/Amsterdam:20230220T110000 DTSTAMP:20260503T162457 CREATED:20230116T150456Z LAST-MODIFIED:20230116T150457Z UID:1037-1676887200-1676890800@www.popnet.io SUMMARY:POPNET Connects with Gert Stulp DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\n\n \n\n\n\nCollecting personal networks to study social influences on fertility behaviour\n\n\n\nPeople’s social environment is key in explaining their behaviour and preferences\, including how many children people want and have. Unfortunately\, obtaining information on the social environment\, for instance through collecting personal network data\, is difficult and often burdensome to participants. In this talk\, I will first report on my experiences with collecting large personal network data (25 alters) from a representative sample of Dutch women. I’ll discuss the trade-offs researchers face when designing network studies between the burden to respondents and the reliability of characteristics of networks. Data quality is further discussed\, including the usefulness of the concept “friend”. In the second part of the talk\, I use these data to test evolutionary and sociological ideas about the breakdown of kin networks in contemporary populations as an explanation for the fertility decline. I’ll further discuss how personal network data can inform on social influences on fertility behaviour. \n\n\n\n\n\n\n\nAbout Gert Stulp\n\n\n\nGert Stulp is based at the department of Sociology at the University of Groningen. He studies causes of the variation in the number of children people have and would like to have\, and employs diverse methods in his research including personal network data collection\, simulation studies\, and machine learning. URL:https://www.popnet.io/events/popnet-connects-with-gert-stulp/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2023/01/POPNET-Connects-with-Gert-Stulp.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230206T140000 DTEND;TZID=Europe/Amsterdam:20230206T150000 DTSTAMP:20260503T162457 CREATED:20221024T095804Z LAST-MODIFIED:20230113T145850Z UID:963-1675692000-1675695600@www.popnet.io SUMMARY:POPNET Connects with Ozan Candogan DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\n\n \n\n\n\nControlling Epidemic Spread: Reducing Economic Losses with Targeted Closures\n\n\n\nData on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However\, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions\, we study a spatial epidemic model that explicitly accounts for population movements and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020)\, our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline nonteleworkable employment level. By contrast\, uniform restriction policies that achieve the same policy goal permit 3.92–6.25 times less nonteleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic. \n\n\n\n\n\n\n\nAbout Ozan Candogan\n\n\n\nOzan Candogan is a Professor of Operations Management at Chicago Booth. Prior to joining Booth\, he was an Assistant Professor at the Fuqua School of Business where he was a member of the Decision Sciences area. He received his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.Candogan’s main research area is social and economic networks.  His research covers two complementary themes. On one hand\, he investigates the impact of networks on operational decisions: He studies how to leverage network data (such as data on social networks\, mobility networks\, and trading networks) to improve operational decisions (ranging from pricing to inventory management and from information disclosure to facility location)\, and sheds light on the value of such data in different operational settings. On the other hand\, he develops novel approaches and tools for the analysis of complex social and economic systems; and explores their applications to characterization of equilibria and dynamics in games\, study of equilibria and comparative statics in trading networks\, and design of information disclosure policies. His research has applications to operations of online social networks\, ride-sharing platforms\, delivery platforms\, two-sided marketplaces\, supply chains\, and online advertising platforms\, among others. Ozan Candogan is a recipient of the 2022 Revenue Management and Pricing Section Prize\, and a finalist for the 2013 George Nicholson Student Paper Competition and the 2021 M&SOM Service Management SIG Prize. He was also a recipient of the 2009 Siebel Scholarship and the 2012 Microsoft Research Ph.D. Fellowship.  URL:https://www.popnet.io/events/popnet-connects-with-ozan-candogan/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/10/POPNET-Connects-with-Ozan-Candogan.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221212T100000 DTEND;TZID=Europe/Amsterdam:20221212T110000 DTSTAMP:20260503T162457 CREATED:20221103T114159Z LAST-MODIFIED:20221103T114749Z UID:979-1670839200-1670842800@www.popnet.io SUMMARY:POPNET Connects with Sune Lehmann DESCRIPTION:Please register for the seminar via the button below. You will receive a confirmation and link to the meeting via email. \n\n\n\n\nRegister to attend online\n\n\n\n\n \n\n\n\nLife2vec: Predicting personality\, death\, emigration\, and other life-events from embeddings of registry data\n\n\n\nOver the past decade\, machine learning has revolutionised computers’ ability to analyze text through flexible computational models. Beyond text\, emerging transformer-based architectures have shown promise as tools to make sense of a range of multi-variate sequences from protein-structures to weather-forecasts due to their structural similarity to written language. Another type of process which has a strong structural similarity to language is human lives. From one perspective\, lives are simply sequences of events: We are born\, we visit the pediatrician\, we start school\, we move to a new location\, we get married\, and so on. Here\, we use this similarity to adapt innovations from natural language processing to examine the evolution and predictability of human lives based on day-to-day event sequences.  \n\n\n\n\n\n\n\nAbout Sune Lehmann\n\n\n\nSune is a Professor of Networks and Complexity Science at DTU Compute\, Technical University of Denmark. He’s also a Professor of Social Data Science at the Center for Social Data Science (SODAS)\, University of Copenhagen. His work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training\, Sune’s research draws on approaches from the physics of complex systems\, machine learning\, and statistical analysis. He works on large-scale behavioral data and while his primary focus is on modeling complex networks\, his research has made substantial contributions on topics such as human mobility\, sleep\, academic performance\, complex contagion\, epidemic spreading\, and behavior on Twitter. URL:https://www.popnet.io/events/popnet-connects-with-sune-lehmann/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/11/POPNET-Connects-with-Ozan-Candogan-2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221121T120000 DTEND;TZID=Europe/Amsterdam:20221121T130000 DTSTAMP:20260503T162457 CREATED:20221103T113344Z LAST-MODIFIED:20221103T114807Z UID:977-1669032000-1669035600@www.popnet.io SUMMARY:POPNET Connects with Ágnes Backhausz DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\nRegister to attend online\n\n\n\n\n \n\n\n\nThe impact of spatial and social structure on an SIR epidemic on a weighted multilayer network\n\n\n\nThe household model defined and analyzed by Frank Ball consists of households (small cliques of the same size)\, connected to each other with edges of smaller weight. However\, this model does not include dense clusters other than the households themselves\, hence\, for example\, school classes are not represented. Starting from this\, in the current work we were interested in the behaviour of an SIR process on a more complex random graph model\, based on the layer of households\, a layer of schools and workplaces\, the layer representing the spatial structure and a fourth layer representing communal places. We studied the sensitivity of the model for the different parameters\, looked for estimates of the parameters in a simpler case\, and compared different vaccination strategies. Our model and the main results will be presented in the talk. \n\n\n\nJoint work with István Z. Kiss\, Péter L. Simon and György Székely. \n\n\n\n\n\n\n\nAbout Ágnes Backhausz\n\n\n\nÁgnes Backhausz obtained her PhD in 2013 at ELTE Eötvös Loránd University\, Budapest\, Hungary\, with a thesis on preferential attachment random graphs. As a postdoctoral researcher\, she spent two years at Alfred Renyi Institute of Mathematics\, working in the “Limits of Structures” research group. Her research topic includes random graphs\, graph limit theory\, and more recently epidemic spread on random networks. Currently she is an assistant professor at ELTE Eötvös Loránd University\, Budapest\, Hungary\, and also a research fellow at Alfréd Rényi Institute of Mathematics. URL:https://www.popnet.io/events/popnet-connects-with-agnes-backhausz/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/11/POPNET-Connects-with-Ozan-Candogan-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221116T070000 DTEND;TZID=Europe/Amsterdam:20221116T160000 DTSTAMP:20260503T162457 CREATED:20221003T120610Z LAST-MODIFIED:20221003T142621Z UID:940-1668582000-1668614400@www.popnet.io SUMMARY:Network-based study of segregation and social capital in the Netherlands using population-scale social network data derived from official population registers DESCRIPTION:Conference talk by Yuliia Kazmina at the Dutch Demography Day 2022. \n\n\n\nUsing population-scale social network data derived from official population registers\, we propose a network-based study of segregation and social capital in the Netherlands.  \n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups as defined by any individual attribute such as race\, religion\, social class\, etc. Nevertheless\, we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are networked phenomena and should be studied as such. We measure to what extent Dutch residents in different municipalities are exposed to individuals of different socio-economic statuses in diverse social contexts and what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, on the contrary\, what social contexts play a role of socio-economic bubbles. Our findings suggest great heterogeneity in socio-economic assortativity between different social contexts as well as different municipalities.  \n\n\n\nSocial capital can be seen as the value and resources found in social structures which enable collective action. It is most often measured indirectly based on theoretical argumentation using data on its expected outcomes\, such as civic participation or volunteering rates. We determine the relationship between network measures of bridging and bonding social capital and volunteering rates. The results of the regression analyses show a significant relation between rates of social bonding and social capital. Network measures related to social bridging have a significant but weaker and negative impact on social capital. This suggests that the type of social capital must be carefully considered when attempting to measure social capital using networks. Our work presents the first major steps for the measurement of social capital using population-scale network data. The findings can be valuable to anyone measuring social capital in networks\, paving the way for informed decision-making aimed at increasing social capital of\, for example\, minority groups. URL:https://www.popnet.io/events/network-based-study-of-segregation-and-social-capital-in-the-netherlands-using-population-scale-social-network-data-derived-from-official-population-registers/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/10/cropped-bg-header-90.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221103T161500 DTEND;TZID=Europe/Amsterdam:20221103T171500 DTSTAMP:20260503T162457 CREATED:20221003T120109Z LAST-MODIFIED:20221014T153328Z UID:937-1667492100-1667495700@www.popnet.io SUMMARY:Capturing the social fabric: Population-scale socio-economic segregation patterns DESCRIPTION:Conference talk by Yuliia Kazmina at the Odissei Conference for Social Science in the Netherlands 2022 in session 4.1. \n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups as defined by any individual attribute such as race\, religion\, social class\, etc. Nevertheless\, we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are networked phenomena and should be studied as such. In this paper\, we make a methodological contribution that would allow the scholarship and policymakers to move away from a traditional spatial understanding of segregation that ignores interactions beyond neighborhoods and shift the focus of segregation measurement to the social network aspect applied to a diverse set of previously unexplored distinct social contexts. \n\n\n\nThe study is based on the Dutch population register data sourced from multiple existing sub-registers that contain information on formal ties and affiliations of ~17 million legal residents in multiple social contexts such as kinship\, household\, neighborhood\, school\, and work. With the multiplex network of geospatially embedded formal ties in hand\, we aim to observe to what extent areas of social segregation are clustered in geospatially embedded social networks\, and how each network layer contributes to the issue. More specifically\, we measure to what extent Dutch residents in different municipalities are exposed to individuals of different socio-economic statuses in diverse social contexts and what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, on the contrary\, what social contexts play a role of socio-economic bubbles. Our findings suggest great heterogeneity in socio-economic assortativity between different social contexts (the layers of the analysed network) as well as different municipalities. URL:https://www.popnet.io/events/capturing-the-social-fabric-population-scale-socio-economic-segregation-patterns-3/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2022/10/Auditorium83-e1663244527368-1280x300-1.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221103T160000 DTEND;TZID=Europe/Amsterdam:20221103T170000 DTSTAMP:20260503T162457 CREATED:20221103T122720Z LAST-MODIFIED:20221103T122722Z UID:990-1667491200-1667494800@www.popnet.io SUMMARY:The anatomy of a population-scale social network DESCRIPTION:Lecture by Eszter Bokányi for the Network Seminar series of the Learning Planet Institute \n\n\n\n Common large-scale approaches to inferring social structure make use of digital traces such as online social networks or mobile communication data. However\, these networks are often agnostic of node and edge representativity and type. This talk investigates the structure of a social network sourced from administrative registers for an entire population based on family\, household\, work\, school\, and next-door neighbor relations\, alongside rich demographic node attributes. We revisit three of the most common concepts in social network analysis: degree\, closure and distance. We find that observed degrees are the result of a combination of degree distributions in various layers\, disqualifying common explanatory mechanisms such as preferential attachment. Low node-to-node distances are realized through particular edge types that shortcut paths in already clustered areas. Measuring closure across layers shows how we can realistically capture the extent to which people have closed or open network opportunity structures. Finally\, we highlight how people’s network structure varies greatly along demographic axes such as age\, income and level of education. This shows that understanding of both the type of edge and the part of the population that is considered is of great importance. Therefore\, leveraging register data to capture the social structure of a complete population is one of the most fruitful ways forward to obtain actionable insights and ultimately evidence-based policies. URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network-4/ CATEGORIES:Lecture ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/11/LPI-large-no-tagline-twitter.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221027T110000 DTEND;TZID=Europe/Amsterdam:20221027T120000 DTSTAMP:20260503T162457 CREATED:20221013T092705Z LAST-MODIFIED:20221026T141059Z UID:948-1666868400-1666872000@www.popnet.io SUMMARY:POPNET Connects with Vincent Traag DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\nRegister to attend online\n\n\n\n\n \n\n\n\n\nRegister to attend on-site\n\n\n\n\nLocation: Leiden UniverisityLeiden Institute of Advanced Computer Science (LIACS)\, Room 403Niels Bohrweg 12333 CA Leiden \n\n\n\nLarge network community detection by fast label propagation\n\n\n\nMany networks exhibit some community structure. There exists a wide variety of approaches to detect communities in networks\, each offering different interpretations and associated algorithms. For large networks\, there is the additional requirement of speed. In this context\, the so-called label propagation algorithm (LPA) was proposed\, which runs in near linear time. In partitions uncovered by LPA\, each node is ensured to have most links to its assigned community. We here propose a fast variant of LPA (FLPA) that is based on processing a queue of nodes whose neighbourhood recently changed. We test FLPA exhaustively on benchmark networks and empirical networks\, finding that it runs up to 700 times faster than LPA. In partitions found by FLPA\, we prove that each node is again guaranteed to have most links to its assigned community. Our results show that FLPA is generally preferable to LPA. \n\n\n\nAbout Vincent Traag\n\n\n\n\n\n\n\nVincent Traag is a senior researcher at the Centre for Science and Technology Studies (CWTS) of Leiden University in the Netherlands. He leads the research line on modelling the research system within the Quantitative Science Studies (QSS) research group. His main interests are mathematical models in the social sciences with a focus on (social) networks. In addition to his scientific research\, Traag also acts as a bibliometric consultant at the CWTS. \n\n\n\nTraag obtained his Master in sociology (cum laude) from the University of Amsterdam (2008). Coming from a computer science background\, and taking up mathematics during his studies in sociology\, he went on to obtain a PhD in applied mathematics in Louvain-la-Neuve\, Belgium (2013). During his PhD he studied methods for detecting communities in complex networks\, resulting in a Python software package. In addition\, he applied this methodology in several fields across the (social) sciences\, ranging from citation networks to international relations. He joined the CWTS in 2015. URL:https://www.popnet.io/events/popnet-connects-with-vincent-traag/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/10/POPNET-Connects-with-Vincent-Traag.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20221021T114500 DTEND;TZID=Europe/Amsterdam:20221021T120000 DTSTAMP:20260503T162457 CREATED:20221014T153015Z LAST-MODIFIED:20221110T100100Z UID:954-1666352700-1666353600@www.popnet.io SUMMARY:Excess closure in a multilayer population-scale social network DESCRIPTION:Lecture by Eszter Bokànyi at the Conference on Complex Systems \n\n\n\nRecent studies on large-scale social networks successfully utilise the growing abundance of digital data sources such as online social networks or mobile communication datasets to uncover fundamental insights on human interaction [1\, 2\, 3]. \n\n\n\nHowever\, in most of these social network data sources\, the sample of people that are represented by the nodes is biased\, and lack of demographic data makes it hard to assess representativity. Moreover\, it is often not clear what exact social relations these online or communication ties represent\, thus\, it is difficult to interpret findings when the goal is to derive meaningful conclusions about people’s social ties [4]. \n\n\n\nWe overcome a number of these drawbacks by presenting a thorough analysis of the complete structure of a 17M node population-scale social network of the Netherlands containing roughly 1.6B edges. This network is derived from highly curated official data sources of the country’s national statistics institute and includes every registered resident in 2018. The edges cover several social relationships: family\, household\, work\, school\, and neighbor. We model each of these edge types as a layer of a node-aligned multilayer network. \n\n\n\nIn addition\, we have rich individual-level demographic and socio-economic attributes on the nodes (people) available. We consider the network to be a representation of the social opportunity structure in the Netherlands. \n\n\n\nHere\, we present the first results that show how this population-scale social network is markedly different from many of the large-scale social networks we typically study and reflect on the consequences for computational social science. Below\, we in particular do so by revisiting the well-known concept of closure. \n\n\n\nClosure is important because individuals have very different resource structures encoded into their social relationships throughout their lives or across demographic groups\, which affects their access to opportunities and information [5]. However\, if we choose to measure closure in a complete population scale social network through traditional local clustering coefficient on the separate layers\, we would get values close to 1. By unioning edges from all layers\, despite the average local clustering coefficient low- ering to 0.40\, it is still unable to resolve potential overlaps or bridges between edges from different layers in people’s egonetworks. \n\n\n\nTo overcome this problem\, we propose a normalized clustering coefficient that we call excess closure\, that fully exploits the multilayer structure of the networks\, and captures the fraction of triangles in people’s social circles that span across multiple types of relationships. \n\n\n\nFigure 1 shows how degree and excess closure change with age (a demographic attribute) in the population. Young children have low degrees and very high excess closure since they are only part of family\, neighborhood\, and household structures. Subsequent levels of education paired with working opportunities come with both an increasing median degree\, and decreasing excess closure\, reaching its minimum Fig. 1. Median degree (red) and median excess closure (blue) in ego networks of people of a certain age. Shaded areas are the 25th and 75th percentiles for each age year. around the university age. Working years are characterized by a slight increase in closure\, and gradually decreasing degree\, giving place to low degrees and increased closure in retirement years. \n\n\n\nOur new normalized multilayer clustering coefficient measure excess closure helps to analyse complete large-scale social networks. The measure captures overlap and bridging between edges of different types in the egonetwork of an individual. We find that excess closure varies across demographic groups as well as throughout people’s lives and it gives a more finegrained understanding of closure in multi-layer population-scale social network data. Our results show a sharp transition from closed to open network structures as young adults engage in higher levels of education\, and a reverse process as people retire. These measurements are first steps in building both methods and universal insights on the rich network structure of highly curated population-level network datasets. \n\n\n\nFig. 1. Median degree (red) and median excess closure (blue) in ego networks of people of a certain age. Shaded areas are the 25th and 75th percentiles for each age year.\n\n\n\nReferences [1] N. Eagle\, A. S. Pentland\, and D. Lazer. “Inferring Friendship Network Structure by Using Mobile Phone Data.” In: Proceedings of the National Academy of Sciences of the United States of America 106.36 (2009)\, pp. 15274–15278. [2] P. S. Park\, J. E. Blumenstock\, and M. W. Macy. “The Strength of Long-Range Ties in Population-Scale Social Networks”. In: Science 362.6421 (2018)\, pp. 1410–1413. [3] M. Bailey et al. “Social Connectedness: Measurement\, Determinants\, and Effects”. In: Journal of Economic Perspectives 32.3 (2018)\, pp. 259–280. [4] D. Lazer et al. “Meaningful Measures of Human Society in the Twenty-First Century”. In: Nature 595.7866 (2021)\, pp. 189–196. [5] G. T ́oth et al. “Inequality Is Rising Where Social Network Segregation Interacts with Urban Topology”. In: Nature Communications 12.1 (2021)\, p. 1143 URL:https://www.popnet.io/events/excess-closure-in-a-multilayer-population-scale-social-network/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/10/CCS.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220919T100000 DTEND;TZID=Europe/Amsterdam:20220919T110000 DTSTAMP:20260503T162457 CREATED:20220623T115514Z LAST-MODIFIED:20220905T080707Z UID:872-1663581600-1663585200@www.popnet.io SUMMARY:POPNET Connects with Floris Vermeulen DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\n\nWhat do big data analytics reveal about neighbourhood organisational vitality?\n\n\n\nBig data from images captured by Google Street View (GSV) can be used to analyse the extent to which the built environment impacts the survival rate of neighbourhood-based social organisations in Amsterdam\, the Netherlands. These organisations are important building blocks for social life in urban neighbourhoods. Examining these organisations’ relationships with their environment has been a useful way to study their vitality. To extract data on built environment features from GSV images\, we applied a deep learning model\, DeepLabv3+. Using elastic net regression we were able to test the relationship between the built environment empirically – distinguishing between car-related\, walking-related and mixed-use land infrastructure – and the survival of neighbourhood organisations. This testing approach is novel\, to our knowledge not yet having been applied in Urban Studies. Besides revealing the effects of built environment features on the social life between buildings\, our study points to the value of easily applicable observational big data. Data captured by GSV and other recently developed methods offer researchers the opportunity to conduct detailed yet relatively swift and inexpensive studies without resorting to overly coarse or common subjective measurements. \n\n\n\n\nRead the full article\n\n\n\n\nAbout Floris Vermeulen\n\n\n\n\n\n\n\nDr. Floris Vermeulen is associate professor (universitair hoofddocent) at the department of political science at the University of Amsterdam . He has been chair of the department of Political Science (2015-1017) and co-director of the Institute for Migration and Ethnic Studies (IMES) (2011-2014) and co-programme group leader of Challenges to Democratic Representation of the Amsterdam Institute of Social Science Research (AISSR) (2011-2014). He studied Economic and Social History at the University of Amsterdam. His dissertation (Cum Laude) was published in the IMISCOE-AUP publication programme\, entitled The immigrant organising process. Turkish organisations in Amsterdam and Berlin and Surinamese organisations in Amsterdam 1960-2000. URL:https://www.popnet.io/events/popnet-connects-with-floris-vermeulen/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/06/POPNET-Connects-with-Floris-Vermeulen.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220913T151600 DTEND;TZID=Europe/Amsterdam:20220913T153600 DTSTAMP:20260503T162457 CREATED:20220909T144902Z LAST-MODIFIED:20220912T075646Z UID:932-1663082160-1663083360@www.popnet.io SUMMARY:The Small-World structure of a population-scale social network DESCRIPTION:Lecture by Frank Takes\, Eszter Bokànyi\, and Eelke Heemskerk at the 6th European Conference on Social Networks (EUSN 2022). \n\n\n\nThe analysis of social networks at the level of an entire population provides a unique opportunity to revisit what is perhaps the most fundamental universal finding in the field: the small world phenomenon. Popularized as “six degrees of separation”\, it refers to the remarkably low average node-to-node distances and typically high amount of clustering observed in real-world social networks. \n\n\n\nIn this talk we revisit this concept from the perspective of the multilayer population-scale social network of the Netherlands consisting of over 17 million people and five characteristic layers of connectivity based on family\, household\, school work and neighborhood relations. By means of a comparison with artificially simulated networks originating from the well-known Watts-Strogatz and Newman-Watts-Strogatz models\, we show that each type of connectivity (i.e. each layer) in the considered population-scale social network has a different characteristic function in realizing the small-world structure of the network. In particular\, we show how highly clustered family relations form the backbone of the network\, akin to the initial regular (ring-shaped) graph in the WS model. Then\, work and school relations primarily act as “random” bridges between different parts of the clustered (yet\, on its own not-so-small-world) family network. Such edges are ultimately crucial for realizing the actual small-world connectivity patterns in the complete population-scale social network.  \n\n\n\nApart from the explicit multilayer aspect of our population-scale social network data\, we can also take advantage of node attributes (i.e.\, people’s demographic characteristics) in understanding how the low average distances of the network are realized. We find distinct patterns of assortativity between node pairs that are unconnected in one layer (e.g.\, family) and connected in the other (e.g.\, work). Subsequently\, these patterns are used to improve the rewiring step of the Watts-Strogatz model in an attempt to more accurately represent our population-social network data. Specifically\, this step takes into account empirical patterns of homophily\, utilizing the extent to which connections are more frequently formed between nodes with close spatial and similar demographic attributes\, such as level of education. \n\n\n\nOn the theoretical side\, the talk provides insights into the relevance of the WS model\, more than 20 years after its inception. Moreover\, it highlights the importance of multilayer approaches in explaining the connectivity of multilayer networked systems in the real world\, as well as the relation between similarity of node attributes and links spanning larger distances. Substantively and empirically\, the talk contributes to an understanding of how distances are bridged in a social network. The work has important implications in processes that may take place on networks\, such as diffusion of information\, dissemination of resources\, or epidemic spreading. URL:https://www.popnet.io/events/the-small-world-structure-of-a-population-scale-social-network/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/EUSN2022-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220913T133900 DTEND;TZID=Europe/Amsterdam:20220913T135900 DTSTAMP:20260503T162457 CREATED:20220909T144434Z LAST-MODIFIED:20220912T075734Z UID:928-1663076340-1663077540@www.popnet.io SUMMARY:Measuring social capital in a population-scale social network DESCRIPTION:Lecture by Bart de Zoete\, Frank Takes\, Eelke Heemskerk\, Eszter Bokànyi and Yuliia Kazmina at 6th European Conference on Social Networks (EUSN 2022). \n\n\n\nIn this work we consider the measurement of social capital using population-scale social network data in the Netherlands. Social capital can be seen as the value and resources found in social structures which enable collective action. Having been linked to many societal phenomena\, social capital has become a cornerstone of social science. It is most often measured indirectly using data on its expected outcomes\, such as civic participation. Another approach\, which we will utilize in this work\, is to use social networks\, which can capture the network structural aspect of social capital. However\, with traditional social network data\, the network aspect can be problematic due to data quality and completeness issues.In this work\, we bring large-scale and high quality social network data and data on four key social capital outcomes together\, in order to for the first time at the scale of an entire population assess the power of network measures of social capital such as average degree and attribute assortativity. We consider a population-scale social network with formal ties (e.g.\, family\, work\, school\, and neighbor relations) of the entire Dutch population. This network has unique properties that make it highly interesting and well-suited for the measurement of social capital\, and its validation. Indeed\, the network contains various node attributes that can be used to group people by the geographical neighborhood in which they reside. This in turn makes it possible to use existing neighborhood data (i.e.\, proxies of community social capital) to validate our measure. Various statistics about Dutch neighborhoods are publicly available\, some of which relate to common social capital outcomes. We represent four social capital outcomes using the percentage of people with good perceived health\, that do volunteer work\, that receive social assistance benefits from the government\, and the number of reported violent crimes per one thousand people in the neighborhood. We use regression models to assess the precise relation between network measures and these social capital outcomes. The results show that all four conceptualizations are to some extent measurable through structural network measures. Our work presents the first major steps for the measurement of social capital using population-scale network data. The findings can be valuable to anyone measuring social capital in networks\, paving the way for informed decision making aimed at increasing social capital of\, for example\, minority groups. URL:https://www.popnet.io/events/measuring-social-capital-in-a-population-scale-social-network/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/EUSN2022-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220913T131600 DTEND;TZID=Europe/Amsterdam:20220913T133600 DTSTAMP:20260503T162457 CREATED:20220909T144142Z LAST-MODIFIED:20220909T144143Z UID:924-1663074960-1663076160@www.popnet.io SUMMARY:Capturing the social fabric: Population-scale socio-economic segregation patterns DESCRIPTION:Lecture by Yuliia Kazmina\, Eszter Bokànyi\, Eelke Heemskerk and Frank Takes at the 6th European Conference on Social Networks (EUSN 2022). \n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups as defined by any individual attribute such as race\, religion\, social class\, etc. Nevertheless\, we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are networked phenomena and should be studied as such. In this paper\, we make a methodological contribution that would allow the scholarship and policymakers to move away from a traditional spatial understanding of segregation that ignores interactions beyond neighborhoods and shift the focus of segregation measurement to the social network aspect applied to a diverse set of previously unexplored distinct social contexts. The study is based on the Dutch population register data sourced from multiple existing sub-registers that contain information on formal ties and affiliations of ~17 million legal residents in multiple social contexts such as kinship\, household\, neighborhood\, school\, and work. With the multiplex network of geospatially embedded formal ties in hand\, we aim to observe to what extent areas of social segregation are clustered in geospatially embedded social networks\, and how each network layer contributes to the issue. More specifically\, we measure to what extent Dutch residents in different municipalities are exposed to individuals of different socio-economic statuses in diverse social contexts and what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, on the contrary\, what social contexts play a role of socio-economic bubbles. Our findings suggest great heterogeneity in socio-economic assortativity between different social contexts (the layers of the analysed network) as well as different municipalities. URL:https://www.popnet.io/events/capturing-the-social-fabric-population-scale-socio-economic-segregation-patterns-2/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/EUSN2022-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220913T104600 DTEND;TZID=Europe/Amsterdam:20220913T110600 DTSTAMP:20260503T162457 CREATED:20220909T143634Z LAST-MODIFIED:20220909T143830Z UID:922-1663065960-1663067160@www.popnet.io SUMMARY:Anonymity of multi-hop neighborhoods in social networks DESCRIPTION:Lecture by Rachel de Jong\, Frank Takes and Mark van der Loo at the 6th European Conference on Social Networks (EUSN 2022). \n\n\n\nIntroduction & Goal. Sharing large-scale social network datasets is advantageous for the development of computational social science\, since studying and replicating findings on such datasets is key to understanding and modeling various social phenomena [1\, 2]. Following the principles of widely implemented privacy laws such as GDPR\, such datasets need to be anonymous\, which means that people should not be identifiable by someone with a realistic amount of background knowledge. This work focuses on a method to assess this so-called risk of disclosure\, by measuring the anonymity of individuals in networks based on their structural position within the network. \n\n\n\nPrevious work has focussed on measuring anonymity using only the direct surroundings of a node [3]. However\, in [4] it is shown that when a possible attacker has information about a larger neighborhood beyond these direct surroundings\, this could drastically decrease the anonymity of the individual. Therefore\, in this work\, we present a novel approach that extends these two earlier works into a parametrized measure that can serve as a lower bound for the expected anonymity at different levels of knowledge of the attacker. On both modeled and real-world social network data\, we demonstrate that if an attacker has perfect information about what we call multi-hop neighborhoods\, the anonymity of individuals in the social network is severely compromised. This has serious implications for any social science researcher sharing social network data with other parties. \n\n\n\nApproach. We measure the anonymity by partitioning the set of nodes of a given social network into equivalence classes. We define equivalence by using the measure of d–k-anonymity\, where two nodes are d-equivalent if 1) their respective d-hop neighborhoods (i.e.\, neighborhoods up to distance d of the node) are isomorphic\, and 2) there is an isomorphism mapping the two compared nodes onto each other. Next\, following [3]\, we define a node as unique if it has no equivalent nodes in the network.  \n\n\n\nTo understand anonymity of individuals in real-world networks\, we measure structural anonymity in various known graph models (Erdős–Rényi (ER) and Watts Strogatz (WS)) and a range of empirical network datasets. We investigate anonymity for increasingly larger hop neighborhoods\, and therewith different attacker knowledge scenarios. This improves upon [3] because we allow for larger-hop neighborhoods\, and upon [4] because we assume perfect information about connectivity of individuals up to a certain distance. URL:https://www.popnet.io/events/anonymity-of-multi-hop-neighborhoods-in-social-networks-2/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/EUSN2022-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220913T102300 DTEND;TZID=Europe/Amsterdam:20220913T104300 DTSTAMP:20260503T162457 CREATED:20220909T143221Z LAST-MODIFIED:20220909T143725Z UID:920-1663064580-1663065780@www.popnet.io SUMMARY:The anatomy of a population-scale social network DESCRIPTION:Lecture by Eszter Bokànyi\, Eelke Heemskerk\, Yuliia Kazmina and Frank Takes at the 6th European Conference on Social Networks (EUSN 2022). \n\n\n\nThe analysis of large-scale societal networks has recently seen tremendous growth\, in part because of the relative abundance of digital data sources such as online social networks or mobile communication datasets. However\, most of these data sources lack demographic data on users or are uncertain with respect to the representativity of the user sample. Moreover\, it is often not clear what exact social relations these online or communication ties represent\, thus\, it is difficult to interpret findings. This talk will attempt to overcome a number of these drawbacks by presenting a thorough overview of the structure of a 17M node multilayer population-scale social network of the Netherlands containing roughly 1.6B edges derived from highly curated official data sources of CBS Netherlands. First\, we show how the degree distribution of this network is a composition of the degree distributions of the different types of edges. In the overall degree distribution\, we find a characteristic value that is in sharp contrast to the scale-free or other fat-tailed distributions found in online social networks or communication networks. Second\, we discuss different types of clustering in this multilayer network\, and show how closed or open network structures emerge for people of certain ages. In particular\, we introduce a normalized multilayer clustering coefficient that we call excess closure\, that captures the fraction of triangles in people’s social circles that span across multiple types of relationships. Finally\, we show that long-range ties that span large distances are very scarce in this network\, which is in contrast to findings in online social networks\, and does not promote fast and efficient diffusion processes over this structure\, despite average path lengths being low. Our measurements are first steps in building both methods and universal insights on the rich network structure of highly curated population-level network datasets. URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network-3/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/EUSN2022-1.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220721T190000 DTEND;TZID=Europe/Amsterdam:20220721T200000 DTSTAMP:20260503T162457 CREATED:20220704T090827Z LAST-MODIFIED:20220704T112430Z UID:892-1658430000-1658433600@www.popnet.io SUMMARY:The anatomy of a population-scale social network DESCRIPTION:Conference poster presentation by Eszter Bokányi at IC2S2 at the Harper Center of the Booth School of Business at the University of Chicago. \n\n\n\nAuthors: Eszter Bokanyi\, Yuliia Kazmina\, Rachel de Jong\, Frank Takes and Eelke Heemskerk \n\n\n\nThe analysis of large-scale societal networks has recently seen tremendous growth\, in part because of the relative abundance of digital data sources such as online social networks or mobile communication datasets1–3. However\, most of these data sources lack demographic data on users or are uncertain with respect to the representativity of the user sample. Moreover\, it is often not clear what exact social relations these online or communication ties represent\, thus\, it is difficult to interpret findings4. \n\n\n\nWe overcome a number of these drawbacks by presenting a thorough overview of the structure of a 17M node population-scale social network of a European country containing roughly 1.6B edges. This network is derived from highly curated official data sources of the country’s national statistics institute. As such\, it includes every resident registered on a certain day in 2018. In addition\, rich individual-level demographic and socio-economic attributes on the nodes are  available alongside the network structure\, as well as the precise type of each social relationship we observe: family\, household\, work\, school\, or neighbor relationship\, each extracted from country-level register data. Just as a typical (online) social network data may suffer from missing connections\, the studied population-scale social network data may miss informal friendship connections not captured in the formal ties in this network. However\, we know that we have precisely all nodes (people)\, and we know that for the types of connections that we have\, data is very complete\, which is a unique setting in social network analysis research. In this work\, we present first results of how such a high quality population scale social network is markedly different from many of the large scale social networks we typically study. Below\, we in particular do so by revisiting the well-known concept of closure in a population-scale social network context. \n\n\n\nFirst\, we show how the degree distribution of this network is a composition of the degree distributions of the different types of edges. In the overall degree distribution\, we find a characteristic value that is in sharp contrast to the scale-free or other fat-tailed distributions found in online social networks or communication networks5. Second\, we discuss different types of clustering in this multilayer network\, and show how closed or open network structures emerge for people of certain ages. In particular\, we introduce a normalized multilayer clustering coefficient that we call excess closure\, that captures the fraction of triangles in people’s social circles that span across multiple types of relationships.  \n\n\n\nFigure 1 shows how degree and excess closure change with age (a demographic attribute) in the population. Young children have low degrees and very high excess closure since they are only part of family\, neighborhood\, and household structures. Subsequent levels of education paired with working opportunities come with both an increasing median degree\, and decreasing excess closure\, reaching its minimum around the university age. Working years are characterized by a slight increase in closure\, and gradually decreasing degree\, giving place to low degrees and increased closure in retirement years. Finally\, we find that long-range ties that span large distances are very scarce in this network\, only 0.02% of all edges not being part of any triangles\, which is in contrast to findings in online social networks\, and does not promote fast and efficient diffusion processes over this structure. \n\n\n\n\n\n\n\nFigure 1. Median degree (red) and median excess closure (blue) in ego networks of people of a certain  age. Shaded areas correspond to the 25th and 75th percentiles for each age year. \n\n\n\nConcluding\, our results show a sharp transition from closed to open network structures as young adults engage in higher levels of education\, and a reverse process as people retire. The findings empirically confirm using large-scale data that individuals have very different resource structures throughout their lives\, which affects their access to opportunities and information6. Our measurements are first steps in building both methods and universal insights on the rich network structure of highly curated population-level network datasets. \n\n\n\nReferences\n\n\n\n1. Eagle\, N.\, Pentland\, A. S. & Lazer\, D. Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. U. S. A. 106\, 15274–15278 (2009). \n\n\n\n2. Park\, P. S.\, Blumenstock\, J. E. & Macy\, M. W. The strength of long-range ties in population-scale social networks. Science 362\, 1410–1413 (2018). \n\n\n\n3. Bailey\, M.\, Cao\, R.\, Kuchler\, T.\, Stroebel\, J. & Wong\, A. Social Connectedness: Measurement\, Determinants\, and Effects. J. Econ. Perspect. 32\, 259–280 (2018). \n\n\n\n4. Lazer\, D. et al. Meaningful measures of human society in the twenty-first century. Nature 595\, 189–196 (2021). \n\n\n\n5. Onnela\, J.-P. et al. Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. 104\, 7332–7336 (2007). \n\n\n\n6. Tóth\, G. et al. Inequality is rising where social network segregation interacts with urban topology. Nat. Commun. 12\, 1143 (2021). URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network-2/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/07/ic2s2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220720T192000 DTEND;TZID=Europe/Amsterdam:20220720T202000 DTSTAMP:20260503T162457 CREATED:20220704T103548Z LAST-MODIFIED:20220905T080720Z UID:901-1658344800-1658348400@www.popnet.io SUMMARY:Anonymity of Multi-hop Neighborhoods in Social Networks DESCRIPTION:Conference poster presentation by Rachel de Jong at Harper Center of the Booth School of Business at the University of Chicago. \n\n\n\nAuthors: Rachel de Jong\, Mark van der Loo and Frank Takes \n\n\n\nIntroduction & Goal. Sharing large-scale social network datasets is advantageous for the development of computational social science\, since studying and replicating findings on such datasets is key to understanding and modeling various social phenomena [1\, 2]. Following the principles of widely implemented privacy laws such as GDPR\, such datasets need to be anonymous\, which means that people should not be identifiable by someone with a realistic amount of background knowledge. This work focuses on a method to assess this so-called risk of disclosure\, by measuring the anonymity of individuals in networks based on their structural position within the network. \n\n\n\nPrevious work has focussed on measuring anonymity using only the direct surroundings of a node [3]. However\, in [4] it is shown that when a possible attacker has information about a larger neighborhood beyond these direct surroundings\, this could drastically decrease the anonymity of the individual. Therefore\, in this work\, we present a novel approach that extends these two earlier works into a parametrized measure that can serve as a lower bound for the expected anonymity at different levels of knowledge of the attacker. On both modeled and real-world social network data\, we demonstrate that if an attacker has perfect information about what we call multi-hop neighborhoods\, the anonymity of individuals in the social network is severely compromised. This has serious implications for any social science researcher sharing social network data with other parties. \n\n\n\nApproach. We measure the anonymity by partitioning the set of nodes of a given social network into equivalence classes. We define equivalence by using the measure of d–k-anonymity\, where two nodes are d-equivalent if 1) their respective d-hop neighborhoods (i.e.\, neighborhoods up to distance d of the node) are isomorphic\, and 2) there is an isomorphism mapping the two compared nodes onto each other. Next\, following [3]\, we define a node as unique if it has no equivalent nodes in the network.  \n\n\n\nTo understand anonymity of individuals in real-world networks\, we measure structural anonymity in various known graph models (Erdős–Rényi (ER) and Watts Strogatz (WS)) and a range of empirical network datasets. We investigate anonymity for increasingly larger hop neighborhoods\, and therewith different attacker knowledge scenarios. This improves upon [3] because we allow for larger-hop neighborhoods\, and upon [4] because we assume perfect information about connectivity of individuals up to a certain distance. \n\n\n\nResults. Figure 1 shows the fraction of unique nodes as a function of the number of nodes n and the average degree. Blue indicates a small fraction of unique nodes\, thus\, high anonymity\, and red indicates a large fraction of unique nodes\, thus\, low anonymity. In the case where d=1\, so in the leftmost column of Figure 1\, our work reproduces precisely the findings in [3]. However\, most importantly\, for larger d-hop neighborhoods\, shown in the middle and rightmost columns of Figure 1\, we see that the uniqueness landscape changes completely. The number of unique nodes\, and its dependence on n and the average degree\, both change drastically. This holds for both models: the fraction of unique nodes becomes high for networks with lower average degrees\, and increasing the network size has less effect on the fraction of unique nodes than for d=1. We conclude that increasing the distance therefore radically decreases the overall anonymity of nodes in the network. \n\n\n\nIn Figure 2\, we summarize our findings for various empirical networks with sizes ranging from 167 to 19.7K nodes. For 10 different real-world networks\, we observe behavior in three categories: 1) high anonymity at d ≥ 1\, 2) high anonymity at d=1\, low anonymity at d ≥ 2 and 3) low anonymity at d=1. Despite currently being publicly available for research\, for most network datasets a large fraction of nodes is uniquely identifiable when information about the 1-hop neighborhood is known. When information about 2-hop neighborhoods is known\, this fraction increases drastically; more entities represented in the network can be uniquely identified and are thus not anonymous. \n\n\n\nConclusions. Our results show that if an attacker has perfect information about multi-hop neighborhoods\, even just at distance two\, then this can drastically reduce the anonymity of nodes in networks\, as observed for the network models and the empirical networks analyzed in our experiments. Since it is realistic for an attacker to obtain some (but not always all) information about larger-hop neighborhoods\, one cannot dismiss the de-anonymizing effects of network structure surrounding a node for d ≥ 2. In future work\, we will explore the effect of possible incomplete knowledge of neighborhood structure. Moreover\, we will investigate how by using small perturbations\, networks can in fact be made fully d-k-anonymous.  \n\n\n\nReferences\n\n\n\nLazer\, D.\, et al. (2020). Computational social science: Obstacles and opportunities. Science 369.6507: 1060-1062.van der Laan\, J. and E.\, de Jonge (2017). Producing official statistics from network data. In Proceedings of the 6th International Conference on Complex Networks and Their Applications\, pp. 288-289.Romanini\, D.\, Lehmann\, S. & Kivelä\, M. (2021). Privacy and uniqueness of neighborhoods in social networks. Scientific Reports 11: 20104.Hay\, M.\,  Miklau G.\, Jensen\, D.\, Towsley D.\, Weis P. (2008). Resisting Structural Reidentification in Anonymized Social Networks. In Proceedings of the VLDB Endowment\, 1.1\, pp. 102-114.Jérôme Kunegis (2013). KONECT – The Koblenz Network Collection. In Proceedings of the International Conference on World Wide Web Companion\, pp. 1343–1350. Ryan A. Rossi and Nesreen K. Ahmed. (2015). The Network Data Repository with Interactive Graph Analytics and Visualization. In AAAI Conference on Artificial Intelligence\, pp. 4292-4293.Sapiezynski\, P.\, Stopczynski\, A.\, Lassen\, D. D. & Lehmann\, S. (2019). Interaction data from the Copenhagen networks study. Scientific Data 6.1: 315.\n\n\n\n   Figure 1. Fraction of unique nodes in artificial network models. Top: Erdős–Rényi (ER)\, bottom: Watts Strogatz (WS). Size: 100-20\,000 nodes. Average degree 2-100. Distance d from left to right: 1\, 2\, 5.\n\n\n\nFigure 2. Fraction of unique nodes in real-world networks [5\, 6\, 7]\, for different values of distance d. URL:https://www.popnet.io/events/anonymity-of-multi-hop-neighborhoods-in-social-networks/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/07/ic2s2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220718T230000 DTEND;TZID=Europe/Amsterdam:20220722T225959 DTSTAMP:20260503T162457 CREATED:20220704T111256Z LAST-MODIFIED:20220711T113451Z UID:905-1658185200-1658530799@www.popnet.io SUMMARY:Capturing the social fabric: population-scale socio-economic segregation patterns DESCRIPTION:Conference poster presentation by Yuliia Kazmina at Harper Center of the Booth School of Business at the University of Chicago (online talk) \n\n\n\nAuthors: Yuliia Kazmina\, Eszter Bokanyi\, Eelke Heemskerk and Frank Takes \n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups\, defined by individual attributes such as race\, religion or social class. Instead\, in this work we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are in fact networked phenomena and should thus be studied as such [1\,2]. We present a methodological contribution that moves away from a traditional spatial understanding of segregation\, and instead considers segregation measurement within the direct social network of individuals.  \n\n\n\nThe study is based on Dutch population register data sourced from multiple existing registers that contain information on formal ties of ~17 million residents. Data covers multiple social contexts (layers): kinship\, household\, neighborhood\, school\, and work. With the multilayer network of geospatially embedded formal ties in hand\, we study to what extent social segregation is clustered in social networks\, and how each network layer contributes to it. Specifically\, we measure to what extent people are exposed to individuals of different socio-economic statuses (SES) for each of the social contexts. Moreover\, we look at what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, inversely\, what social contexts play a role in sustaining so-called  “socio-economic bubbles”.  \n\n\n\nTo capture socio-economic segregation patterns on a population scale\, we introduce a concept of “social opportunity structures” that builds upon the Opportunity Structure Theory proposed by Ken Roberts [3]. Individual ego networks we observe in this study are assumed to be a realization of a particular aspect of Roberts’ opportunity structures – they represent the anatomy and composition of social circles within which individuals exist\, evolve\, and are required to make successive choices. We aggregate household-level social opportunity structures in each of the selected contexts to the level of a municipality to measure to what extent households of a certain socio-economic status (captured by the equivalised household income) are\, on average\, exposed to households across all income deciles. In this abstract\, we focus on the municipality of Amsterdam. \n\n\n\nEstimated social opportunity structures for each of the selected contexts are represented by what we call social opportunity matrices\, in which the vertical axis represents analyzed households divided into ten income deciles\, sorted in ascending order. Then\, the horizontal axis indicates income deciles of connected households in the increasing order. Each cell at the intersection of two income deciles displays the share of contacts a household of a certain income bracket (on the vertical axis) shares with the households in the income decile on the horizontal axis. Values are normalized by row. The diagonal elements represent the share of contacts each income decile has within its own income bracket. To capture the overall segregation for a particular context\, we measure the extent of link assortativity [4]  with respect to income.  \n\n\n\nFigure 1 presents social opportunity structures with respect to income for the households in the city of Amsterdam (~460k households) in the kinship\, school\, work\, and neighborhood (both administrative neighborhoods typically containing several hundred to thousands of households as well as the ten closest neighbors) contexts. The estimated social opportunity matrices present a number of interesting findings.  \n\n\n\nFirst\, in Fig. 1a we see that all income brackets are highly exposed to the neighbors that belong to the two lowest income deciles in the context of being in the same administrative neighborhood. Second\, once the context is narrowed down to the subset of the ten closest neighboring households only (Fig. 1b)\, the matrix reveals a significantly different pattern: close neighborhood social context is much more assortative with respect to income\, as evidenced by the assortativity value of  0.12 vs 0.04 in the case of the administrative neighborhood.  \n\n\n\nThird\, the family layer (Fig. 1c) exhibits similar income assortativity pattern\, with a high prevalence of within income bracket connectivity with 25-30% of family members living separately from an observed household belonging to the same income bracket.  \n\n\n\nAlthough the overall assortativity in the school layer is again comparable\, the distribution of the preference for the own income class along income range is significantly dissimilar: the strongest preference to be classmates with children and adolescents that belong to the same socio-economic class is observed in the lowest income decile as well as in the richest 10% of the households. Finally\, the workplaces’ (Fig. 1d) assortativity is relatively high\, however\, we do not observe an apparent prevalence of diagonal elements\, likely due to several very large workplaces being present in the data.  \n\n\n\nConcluding\, we find that the analyzed social contexts are highly dissimilar in terms of socio-economic assortativity. The most assortative layer is the family network. Other layers\, while being less assortative overall\, reveal interesting patterns. Close neighbors and small workplaces exhibit highly assortative mixing patterns with respect to income that limits the exposure to individuals from different socio-economic backgrounds. On the other hand\, school networks display relatively lower income assortativity and provide individuals with diverse social contact opportunities.  \n\n\n\nThe broad implication of the present study is the potential to capture and quantify social segregation patterns on a large scale with the ability to distinguish between different social contexts\, advocating the study of multi-layer administrative data for the purposes of obtaining a more global policy-relevant insight into population-scale social cohesion. \n\n\n\nReferences\n\n\n\nFreeman\, L. C. (1978). Segregation in social networks. Sociological Methods & Research 6 (1978): 411 – 429.Dimaggio\, P.\, & Garip\, F. (2012). Network effects and social inequality. Annual Review of Sociology 38:1 (2012): 93-118.Roberts\, K. (1977). The Social Conditions\, Consequences and Limitations of Careers Guidance. British Journal of Guidance & Counselling 5:1 (1977): 1-9.Newman\, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters Vol. 89 (20): 208701.\n\n\n\nFigure 1. Social opportunity structures of the households in Amsterdam\, each subfigure displaying a different context: \n\n\n\na) administrative neighborhood  (assortativity: 0.035)              \n\n\n\n\n\n\n\nb) close neighbors (assortativity: 0.118) \n\n\n\n\n\n\n\nc) family (assortativity: 0.124) \n\n\n\n\n\n\n\nd) school (assortativity: 0.114)                                                  \n\n\n\n\n\n\n\ne) workplace  (assortativity: 0.123) URL:https://www.popnet.io/events/capturing-the-social-fabric-population-scale-socio-economic-segregation-patterns/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/07/ic2s2.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220713T152000 DTEND;TZID=Europe/Amsterdam:20220713T153000 DTSTAMP:20260503T162457 CREATED:20220711T113326Z LAST-MODIFIED:20220711T114705Z UID:911-1657725600-1657726200@www.popnet.io SUMMARY:Capturing socio-economic bubbles DESCRIPTION:Lightning talk by Yuliia Kazmina at Women in Network Science & Diversify NetSci joint satellite \n\n\n\n\n\nSegregation is a widely studied issue traditionally explored from the point of the spatial distribution of different groups as defined by any individual attribute such as race\, religion\, social class\, etc. Nevertheless\, we argue that the issues of persistent segregation\, specifically socio-economic segregation\, are networked phenomena and should be studied as such. In this paper\, we make a methodological contribution that would allow the scholarship and policymakers to move away from a traditional spatial understanding of segregation that ignores interactions beyond neighborhoods and shift the focus of segregation measurement to the social network aspect applied to a diverse set of previously unexplored distinct social contexts. The study is based on the Dutch population register data sourced from multiple existing sub-registers that contain information on formal ties and affiliations of ~17 million legal residents in multiple social contexts such as kinship\, household\, neighborhood\, school\, and work. With the multiplex network of geospatially embedded formal ties in hand\, we aim to observe to what extent areas of social segregation are clustered in geospatially embedded social networks\, and how each network layer contributes to the issue. More specifically\, we measure to what extent Dutch residents in different municipalities are exposed to individuals of different socio-economic statuses in diverse social contexts and what social contexts provide diverse social contact opportunities with respect to the socio-economic status and\, on the contrary\, what social contexts play a role of socio-economic bubbles. Our findings suggest great heterogeneity in socio-economic assortativity between different social contexts (the layers of the analysed network) as well as different municipalities. URL:https://www.popnet.io/events/capturing-socio-economic-bubbles/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2022/07/Lightning-talk-WiNS-Society-Yuliia-Kazmina-scaled.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220616T130000 DTEND;TZID=Europe/Amsterdam:20220616T140000 DTSTAMP:20260503T162457 CREATED:20220523T072954Z LAST-MODIFIED:20220617T114704Z UID:858-1655384400-1655388000@www.popnet.io SUMMARY:Population-scale Social Network Analysis DESCRIPTION:Lecture by Frank Takes for the Interaction Data Lab of the Center for Research and Interdisciplinarity (CRI) in Paris. \n\n\n\n\n\nAbstract\n\n\n\nThis talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. The data stems from country-wide administrative register data\, and has the potential to shed new light on contemporary social scientific problems such as segregation\, inequality\, loneliness and poverty. The talk discusses how the formal links (family\, household\, work\, school and neighbor ties) in this social network require one to critically rethink network science concepts such as the unit of analysis\, measurement errors effects and the boundary specification problem. Moreover\, it allows us to in a unique way revisit the well-known concept of closure and the small-world phenomenon in a population-scale social network context. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population. URL:https://www.popnet.io/events/population-scale-social-network-analysis-5/ CATEGORIES:Lecture ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/05/screen-shot-2020-11-13-at-1.24.22-pm.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220519T083000 DTEND;TZID=Europe/Amsterdam:20220519T160000 DTSTAMP:20260503T162457 CREATED:20220421T123707Z LAST-MODIFIED:20220421T124124Z UID:809-1652949000-1652976000@www.popnet.io SUMMARY:Dutch Network Science Society Symposium 2022 DESCRIPTION:The Dutch Network Science Society brings together researchers in network science in the Netherlands from various disciplines\, including math\, physics\, computer science\, social sciences\, economics and health sciences. There will be a program of invited talks from across the spectrum of research in network science. In addition\, there will be plenty of opportunities for the community to discuss\, socialize and enjoy food and drinks together. \n\n\n\nProgram\n\n\n\n9:30Walk in9:45Opening9:50Uniform sampling of random networks with sequential construction – Ivan Kryven\, Universiteit Utrecht10:25The anatomy of a population-scale social network – Eszter Bokányi\, Universiteit van Amsterdam/ Leiden University11:00Coffee break11:20From graphs to hypergraphs randomization – Tiziano Squartini\, IMT Lucca & Institute for Advanced Study UvA11:55Using machine learning to enhance graph processing algorithms – Ana Lucia Varbanescu\, Universiteit Twente & UvA12:30Lunch13:30Empirically calibrated network simulation as a tool to study social interventions – Christian Steglich\, Rijksuniversiteit Groningen14:05Multiscale network neuroscience: embracing the complexity of the human brain network – Linda Douw\, Vrije Universiteit14:40Industry & Society showcases15:10Coffee break15:40Using network models to describe\, predict\, understand\, and treat mental disorders – Eiko Fried\, Universiteit Leiden16:15Dutch Network Science Society Young Talent Prize Award – Bastian Prasse\, European Centre for Disease Prevention and Control17:00Drinks URL:https://www.popnet.io/events/dutch-network-science-society-symposium-2022/ CATEGORIES:Symposium ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/NetSci.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220517T090000 DTEND;TZID=Europe/Amsterdam:20220517T100000 DTSTAMP:20260503T162457 CREATED:20220511T124947Z LAST-MODIFIED:20220617T113600Z UID:839-1652778000-1652781600@www.popnet.io SUMMARY:POPNET Connects with Tamas David-Barrett DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\n\nStructural microfoundation theory\n\n\n\nYou are my love. You are my sister. You are my friend. A trivial fact of our species’ social life is that human social network edge type vary. This variation is not only important for each of these relationships\, but also for the structure of the social network around us. This talk will outline the theoretical models for what happens to the social network structure when the bulk of these relationships change. Our societies shift from kinship network to friendship networks due to falling fertility\, urbanisation\, and migration. Second\, the talk will offer an overview the existing empirical evidence using large datasets\, and suggest explicit empirical hypotheses. The final part will cover how three further phenomena is predicted by this theory\, and ideas of how to test these: the rise of modern law\, value fundamentalism\, and fake news. \n\n\n\n\n\n\n\nAbout Tamas David-Barrett\n\n\n\nTamas David-Barrett is an evolutionary behavioural scientist\, whose research asks what traits allow humans to live in large and culturally complex societies. He is especially interested in the architecture of social networks\, and the evolutionary origins of social network building traits. Tamas’s structural micro-foundation theory offers a new understanding of human societies\, and brings biological and social science models under a shared umbrella. \n\n\n\nCurrently\, Tamás is based in Oxford where he teaches at Trinity College. He was educated in London\, Cambridge\, Jerusalem\, and Budapest. Before becoming an academic\, he ran a research consultancy and worked all around the planet. He recently finished his book\, Matriocracy: The Science of Gender Rules. He is the host of the State of Species annual lecture\, and is currently working on a new book: How to Think Scientifically\, which tells the natural history of social and scientific truths. URL:https://www.popnet.io/events/popnet-connects-with-tamas-david-barrett/ CATEGORIES:Seminar ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/05/POPNET-Connects-with-Tamas-David-Barrett.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20220512T133000 DTEND;TZID=Europe/Amsterdam:20220512T150000 DTSTAMP:20260503T162457 CREATED:20220426T090638Z LAST-MODIFIED:20220426T090640Z UID:831-1652362200-1652367600@www.popnet.io SUMMARY:The anatomy of a population-scale social network DESCRIPTION:Lecture for the Institute for Analytical Sociology of Linköping University\, by Eszter Bokányi. \n\n\n\nAbstract: The analysis of large-scale societal networks has recently seen tremendous growth\, in part because of the relative abundance of digital data sources such as online social networks or mobile communication datasets. However\, most of these data sources lack demographic data on users or are uncertain with respect to the representativity of the user sample. Moreover\, it is often not clear what exact social relations these online or communication ties represent\, thus\, it is difficult to interpret findings. This talk will attempt to overcome a number of these drawbacks by presenting a thorough overview of the structure of a 17M node multilayer population-scale social network of the Netherlands containing roughly 1.6B edges derived from highly curated official data sources of CBS Netherlands. First\, we show how the degree distribution of this network is a composition of the degree distributions of the different types of edges. In the overall degree distribution\, we find a characteristic value that is in sharp contrast to the scale-free or other fat-tailed distributions found in online social networks or communication networks. Second\, we discuss different types of clustering in this multilayer network\, and show how closed or open network structures emerge for people of certain ages. In particular\, we introduce a normalized multilayer clustering coefficient that we call excess closure\, that captures the fraction of triangles in people’s social circles that span across multiple types of relationships. Finally\, we show that long-range ties that span large distances are very scarce in this network\, which is in contrast to findings in online social networks\, and does not promote fast and efficient diffusion processes over this structure\, despite average path lengths being low. Our measurements are first steps in building both methods and universal insights on the rich network structure of highly curated population-level network datasets. URL:https://www.popnet.io/events/the-anatomy-of-a-population-scale-social-network/ CATEGORIES:Lecture ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/linkoping-university-vector-logo.png END:VEVENT END:VCALENDAR