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:20200329T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20201025T010000 END:STANDARD 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 BEGIN:DAYLIGHT TZOFFSETFROM:+0100 TZOFFSETTO:+0200 TZNAME:CEST DTSTART:20250330T010000 END:DAYLIGHT BEGIN:STANDARD TZOFFSETFROM:+0200 TZOFFSETTO:+0100 TZNAME:CET DTSTART:20251026T010000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20241018T100000 DTEND;TZID=Europe/Amsterdam:20241018T190000 DTSTAMP:20260502T141509 CREATED:20241001T125809Z LAST-MODIFIED:20241009T095143Z UID:1278-1729245600-1729278000@www.popnet.io SUMMARY:Increasing mobility is linked to decreasing cohesion of personal networks overthe lifecourse of an entire population DESCRIPTION:On October 18\, Eszter Bokányi will present her work on “Increasing mobility is linked to decreasing cohesion of personal networks over the lifecourse of an entire population” at the Dutch NetSci Symposium\, which will take place at Eindhoven University of Technology. \n\n\n\n \n\n\n\nAbstract\n\n\n\nGrowing accessibility between places and a changing society foster higher human mobility both daily and long-term\, which results in an increased social connectivity between faraway places. Thus\, geographic displacement creates new connections and at the same time rearranges the existing spatial structure of social networks. Both of these mechanisms are little understood in the existing literature of social network formation processes. Moreover\, up until now\, it has also been a challenge to systematically follow the temporal evolution of an entire population’s social network structure. \n\n\n\nIn this work\, we use a unique longitudinal population-scale network dataset sourced from Statistics Netherlands. This network contains family\, work\, school\, household\, and next-door neighbor connections derived from administrative registers\, that together constitute a multilayer social opportunity structure for all residents of the Netherlands between 2009 and 2022. We follow the patterns of individuals’ network surroundings over time\, and measure size\, closure\, and geographical dispersion of ego networks. Size is captured by degree\, closure by excess closure [1]\, which is based on the node clustering coefficient. Geographical dispersion is given by the average distance from network neighbors\, and the average share of network neighbors in the same municipality or the same next-door neighborhood as the ego. \n\n\n\nThe three findings presented in this work are that while the average size of ego networks stays stable over the observed period\, average closure drops by as much as 10%\, and the average distance from network neighbors grows\, while the average share of network neighbors in the same geographic area decreases. We use multivariate regressions to show that the observed decrease in the closure is significantly linked to the growing geographic dispersion\, thus\, the increasing daily and long-term mobility of people. We control for demographic and socio-economic background including the age\, migrant generation\, income\, and whether people attend school and have employment. The regressions confirm that beyond degree and demographics\, variables that capture people’s mobility are linked to the opening up of individual networks. \n\n\n\nThis work is the first of its kind that aims to map the temporal network of an entire population structure comprehensively. As such\, it offers a starting point for a wide variety of impactful network science research at the level of a complete population. \n\n\n\n\n\n\n\n[1] Bokányi\, E.\, Heemskerk\, E. M.\, & Takes\, F. W. (2023). The anatomy of a population-scale social network. Scientific Reports\, 13(1)\, 9209. URL:https://www.popnet.io/events/increasing-mobility-is-linked-to-decreasing-cohesion-of-personal-networks-overthe-lifecourse-of-an-entire-population/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/NetSci.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20240627T083000 DTEND;TZID=Europe/Amsterdam:20240627T101000 DTSTAMP:20260502T141509 CREATED:20240603T113716Z LAST-MODIFIED:20240603T113832Z UID:1249-1719477000-1719483000@www.popnet.io SUMMARY:Parenthood status of siblings\, half-siblings and cousins and entry into parenthood. A horizontal kinship network approach DESCRIPTION:On June 27\, Vera de Bel\, Alyona Artamonova\, Takayuki Hiraoka\, Mirkka Danielsbacka\, Antti Tanskanen\, Marijtje van Duijn will present their work on “Parenthood status of siblings\, half-siblings and cousins and entry into parenthood. A horizontal kinship network approach” at Sunbelt 2024\, which will take place in at Heriot-Watt University\, Edinburgh. \n\n\n\nAbstract\n\n\n\nHorizontal kinship ties last a lifetime\, and life-course transitions of horizontal kin are known to be interconnected. Prior research\, with a focus on siblings\, has demonstrated their influence on various life events\, including home leaving (Her\, Vergauwen\, & Mortelmans\, 2022)\, marriage and divorce behavior (Buyukkececi & Leopold\, 2021)\, and fertility (Buyukkececi & Leopold\, 2021; Lyngstad & Prskawetz\, 2010). However\, siblings are just one example of individuals’ horizontal kin\, and the impact extends to half-siblings and cousins\, shaping both individual trajectories and those of their relatives. Moreover\, the influence of horizontal kin may vary based on factors such as the degree of relatedness (full siblings\, half-siblings\, or cousins)\, kin’s gender ((half-)brothers\, (half-)sisters\, male or female cousins)\, and lineage (paternal or maternal side of the family). This study seeks to analyze the complete horizontal kinship network to understand how individuals’ entry into parenthood is associated with the parenthood status of their horizontal kin. More specifically\, the study examines whether 1) close kin compared to more distant kin\, 2) female compared to male kin\, and 3) maternal compared to paternal kin have a stronger influence on an individual’s entry into parenthood. Utilizing unique registry data that documents the kinship networks of the entire Dutch (van der Laan\, de Jonge\, Das\, Te Riele\, & Emery\, 2022) and Finnish population\, the study will employ a multi-level model considering the nested structure and dependence of relationships (Snijders & Bosker\, 2012; van Duijn\, 2013). First results and a comparison between the Dutch and Finnish contexts will be presented. URL:https://www.popnet.io/events/parenthood-status-of-siblings-half-siblings-and-cousins-and-entry-into-parenthood-a-horizontal-kinship-network-approach/ CATEGORIES:Conference talk END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20240626T000000 DTEND;TZID=Europe/Amsterdam:20240629T235959 DTSTAMP:20260502T141509 CREATED:20240624T113513Z LAST-MODIFIED:20240624T113516Z UID:1257-1719360000-1719705599@www.popnet.io SUMMARY:POPNET presents their work during Sunbelt Conference 2024 DESCRIPTION:POPNET proudly joins the 44th edition of the Sunbelt Conference in Edinburgh from 24th June to June 30th 2024. The Sunbelt Conference is part of the International Network for Social Network Analysis (INSNA)\, providing an interdisciplinary venue for social scientists\, mathematicians\, computer scientists\, ethnologists\, and others to present current work in the area of social networks. \n\n\n\nExploring Social Network Analysis \n\n\n\nIn alignment with POPNET’s aim to unlock longitudinal social network data on the Dutch population\, this year theme of the Sunbelt 2024 is ‘Networks and Resilience’. Throughout the conference\, various members of POPNET will give talks in the field of social network analysis. Social capital\, network clustering\, and family networks are just a few examples of the topics that will be covered at Sunbelt 2024. \n\n\n\nFind below a list of the titles they will present during the conference: \n\n\n\n\n26 June\, 14:30: “Is Social Capital Good for you?”\, by Heemskerk\, Eelke; Takes\, Frank W. .\n\n\n\n26 June\, 14:30: “Social Network Determinants of Economic Prosperity in a Longitudinal Population-scale Social Network”\, by Kazmina\, Yuliia; Heemskerk\, Eelke; Bokanyi\, Eszter; Takes\, Frank W. .\n\n\n\n27 June\, 8:30: “Parenthood status of siblings\, half-siblings and cousins and entry into parenthood. A horizontal kinship network approach”\, by de Bel\, Vera; Artamonova\, Alyona; Hiraoka\, Takayuki; Danielsbacka\, Mirkka; Tanskanen\, Antti; van Duijn\, Marijtje.\n\n\n\n27 June\, 10:40: “Large and small-scale dynamics of a longitudinal population-scale social network”\, by Bokanyi\, Eszter; Kazmina\, Yuliia; van der Kooij\, Emilia; Takes\, Frank; Heemskerk\, Eelke.\n\n\n\n29 June\, 14:00: “Connectivity and community structure of online and register-based population-scale social networks”\, by Menyhért\, Márton; Bokányi\, Eszter; Corten\, Rens; Heemskerk\, Eelke; Kazmina\, Yuliia; Takes\, Frank W. . URL:https://www.popnet.io/events/popnet-presents-their-work-during-sunbelt-conference-2024/ CATEGORIES:Conference talk END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20230629T213000 DTEND;TZID=Europe/Amsterdam:20230629T225000 DTSTAMP:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20221116T070000 DTEND;TZID=Europe/Amsterdam:20221116T160000 DTSTAMP:20260502T141509 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:20260502T141509 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:20221021T114500 DTEND;TZID=Europe/Amsterdam:20221021T120000 DTSTAMP:20260502T141509 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:20220913T151600 DTEND;TZID=Europe/Amsterdam:20220913T153600 DTSTAMP:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20260502T141509 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:20211209T110000 DTEND;TZID=Europe/Amsterdam:20211209T120000 DTSTAMP:20260502T141509 CREATED:20211012T114552Z LAST-MODIFIED:20211012T114553Z UID:659-1639047600-1639051200@www.popnet.io SUMMARY:ODISSEI Lunch Lecture: Population Scale Social Network Analysis DESCRIPTION:Lecture by Eelke Heemskerk \n\n\n\nPOPNET is a novel digital infrastructure and research community with the aim of unlocking longitudinal social network data on the entire population of the Netherlands for academic research purposes. It enables new exciting research in an anonymized as well as ethically and legally responsible manner. This research may lead to actionable insights into key issues including segregation\, substantive social change\, and UN sustainable development goals such as reducing inequality.Research infrastructureA first-of-a-kind research infrastructure tailored in terms of hard- and software specifically for large-scale social network analysis will be developed. Rich methods from social network analysis and network science will be implemented to unveil new and previously unknown knowledge about the complexity of the Dutch population. This requires the development of an infrastructure that facilitates this kind of research in terms of computation power\, memory and data management.Social network analysis researchPilot social network analysis studies will be performed on the (anonymized) population-scale social network of the Netherlands\, consisting of 17 million nodes (people) and their hundreds of millions of family\, work\, school and neighbourhood links as well as demographic attributes. The research\, which has a strong computational social science character\, will be done in parallel with the building of the research infrastructure to ensure alignment of the developed platform and researcher needs. \n\n\n\nAbout the ODISSEI Lunch Lecture Series\n\n\n\nThe ODISSEI Lunch Lectures highlight methodological issues and innovations in Social Science. URL:https://www.popnet.io/events/odissei-lunch-lecture-population-scale-social-network-analysis/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2021/05/alina-grubnyak-ZiQkhI7417A-unsplash-scaled.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20211118T110000 DTEND;TZID=Europe/Amsterdam:20211118T163000 DTSTAMP:20260502T141509 CREATED:20211025T094105Z LAST-MODIFIED:20220112T135147Z UID:669-1637233200-1637253000@www.popnet.io SUMMARY:ODISSEI Community Conference 2021 DESCRIPTION:Missed the lecture by Frank Takes at the ODISSEI Community Conference 2021? You can view the full talk here at 1:01:29. \n\n\n\nCo-director of POPNET Frank Takes will be speaking at the ODISSEI Community Conference during the session “Innovating Computational Social Science research projects”. The session showcases current computational research projects that are conducted within the social sciences. \n\n\n\nAbout the ODISSEI Community Conference: \n\n\n\nThe Community Conference 2021 will be an opportunity to meet those who are building and using ODISSEI in person\, through a series of highly interactive and stimulating sessions designed to encourage collaborations and inspire new lines of research. Sessions will encompass everything from the latest developments in survey science through to projects utilizing the ODISSEI Secure Supercomputer. Frank Pijpers\, professor by special appointment of Complexity for Official Statistics (CBS and UvA)\, will give a keynote lecture. The conference will take place in the Muntgebouw in Utrecht on Thursday\, 18 November from 12:00 to 17:30 hours. Attendance is free of charge. \n\n\n\nProgramme: \n\n\n\n12:00Lunch with ODISSEI Facilities Fair13:00Welcome by Pearl Dykstra (ODISSEI)13:05Keynote: ‘Network Reconstruction: Why and How?’– Frank P. Pijpers (Statistics Netherlands (CBS) and University of Amsterdam)13:45Innovating Computational Social Science research projectsThis session showcases current computational research projects that are conducted within the social sciences. They use pseudonymised CBS microdata for their analysis with a variety of innovating approaches: on the ODISSEI Secure Supercomputer\, in combination with LISS panel data\, or within the new research infrastructure POPNET that is supported by the Platform Digital Infrastructure Social Sciences & Humanities (PDI-SSH). ‘Filling in the blind spots: income and wealth of households’– Marike Knoef (Leiden University)‘Population-scale social network analysis‘– Frank Takes (Leiden University\, POPNET)‘Geographic visualization of childhood opportunities in the Netherlands using the OSSC Secure Supercomputer’– Bastian Ravesteijn (Erasmus University Rotterdam and KansenKaart.nl)14.30Open Science with Secure Data One of the biggest challenges within computational social science is how to share research that is conducted with sensitive data in a secure environment to protect privacy. This session focuses on how researchers are currently dealing with this challenge\, and discusses highly promising new ways to answer present-day ambitions to make computational social science more open and FAIR.‘Collaborating when using sensitive data in a secure environment‘– Bas van der Klaauw (VU Amsterdam)‘The big workaround: an open processing and analysis pipeline for closed data‘– Erik-Jan van Kesteren (Utrecht University and ODISSEI SoDa Team) Respondent: Melanie Imming – independent consultant specialized in Open Science and FAIR data15.15Coffee break15.30Linking large datasets in social science and humanitiesBoth the social sciences and the humanities are making massive strides in digitizing and linking large datasets. How do these fields overlap\, and what are shared challenges? This session will explore current projects and the ways in which social scientists and scholars in the humanities can benefit from each other’s experiences and strengthen each other’s work.Confirmed speaker:‘Creating life course datasets from historical population sources: the case of Suriname 1830-1950‘– Rick MouritsFurther speakers are to be announced.16.15Closing remarks by Pearl Dykstra16.30DrinksPlease note that at the Muntgebouw\, all COVID-regulations\, including those for restaurants\, apply. Those attending the conference are therefore kindly requested to bring their CoronaCheck QR code.\n\n\n\nLocation \n\n\n\nThis year the ODISSEI Community Conference will take place at:MuntgebouwLeidseweg 903531 BG UtrechtView on Google maps URL:https://www.popnet.io/events/odissei-community-conference-2021/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2021/10/pexels-luis-quintero-2774556-1-scaled.jpg END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20211001T120000 DTEND;TZID=Europe/Amsterdam:20211001T170000 DTSTAMP:20260502T141509 CREATED:20211011T092945Z LAST-MODIFIED:20211029T100240Z UID:651-1633089600-1633107600@www.popnet.io SUMMARY:3rd NETWORKS Match Makers Seminar DESCRIPTION:Lecture by Frank Takes \n\n\n\nThe Networks Match Makers seminar series started in October 2020 after a succesfull workshop in January 2020\, called NETWORKS Matchmaking Event. In this series we bring together network scientists from the social and economic sciences with network scientists from mathematics and computer science\, with the goal to explore the opportunity to build synergies. \n\n\n\nThe third seminar takes place in the afternoon of October 1\, 2021\, and will be held online. \n\n\n\nFrank Takes is assistant professor at the computer science department (LIACS) of Leiden University. Frank will talk about ‘Population-scale social network analysis’. \n\n\n\nPopulation-scale social network analysis \n\n\n\nThis talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. The data is sourced from country-wide administrative register data\, enabling the discovery of population-scale insights into a society. I will show how the analysis of a population-scale multilayer network of family\, work\, school\, household and neighborhood relations enables us to revisit the well-known small-world phenomenon from a unique angle. Moreover\, I discuss how the type of formal links in this social network require one to critically rethink network analysis concepts such as the unit of analysis\, measurement errors effects and the boundary specification problem. URL:https://www.popnet.io/events/3rd-networks-match-makers-seminar/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/10/21complex-networks.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20210909T090000 DTEND;TZID=Europe/Amsterdam:20210909T092000 DTSTAMP:20260502T141509 CREATED:20210907T151000Z LAST-MODIFIED:20211029T100303Z UID:631-1631178000-1631179200@www.popnet.io SUMMARY:Population-scale social network analysis DESCRIPTION:Parallel session talk by Frank Takes at European Conference on Social Network EUSN 2021 \n\n\n\nThis work is centered around a population-scale social network analysis study of all 17 million inhabitants of the Netherlands. In the considered (anoymized) population-scale social network\, node and edge information stems from register data: official government registers containing highly curated records on family\, work\, school\, household and neighborhood relations. First\, we discuss how the considered data is fundamentally different from the type of data commonly used to define connectivity in socials networks\, such as survey data\, spatiotemporal proximity data or online social media data. To understand how to derive meaningful insights from the considered more \formal” social ties\, we first revisit some of the fundamental issues in network analysis\, relating to the unit of analysis (Butts 2009)\, measurement errors (Kossinets 2016\, Wang et al. 2012) and the boundary specification problem (Laumann 1989\, Nowell et al. 2018). Second\, we present characteristics of the constructed multilayer social network\, in which 17.2 million nodes are connected through 41.1 million household links\, 233.8 million school links\, 270.2 million family links\, 352.7 million neighbor links\, 566.0 million work links. In total\, there are 1.423 billion unique links between individuals\, as some of the layers overlap. As expected\, the network as a whole has an overall skewed degree distribution and is highly clustered\, the latter in part due to the fact that some layers are in fact projections of underlying two-mode affiliation networks. Third\, a more in-depth analysis of the family layer of this multilayer network dataset reveals the family structure of all 17.2 individuals living in the Netherlands. We present unique statistics on the statistical properties of this population-scale family network\, consisting of directed parent-child relationships. We do so in light of two concrete examples with relevance in the family studies and sociology literature. Purely based on the structure of this network\, we can now for the first time\, at scale\, validate existing findings and hypotheses in this area. In particular\, we look at household composition for children with parents that are no longer together and remarriage behavior of parents with and without children. The two issues above can quantitatively be addressed by investigating at the overlap of the family layer with for example the household layer. Finally\, we demonstrate the advantages and disadvantages of using register data as compared to the use of household survey data in the study of family networks\, and how the interplay between the family layer and other network layers can be used to answer a plethora of other network-driven socio-economic questions of interest. URL:https://www.popnet.io/events/population-scale-social-network-analysis-2/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/09/EUSN-2021.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20210731T170000 DTEND;TZID=Europe/Amsterdam:20210731T183000 DTSTAMP:20260502T141509 CREATED:20210719T074614Z LAST-MODIFIED:20211029T100317Z UID:568-1627750800-1627756200@www.popnet.io SUMMARY:Measuring Anonymity in Complex Networks DESCRIPTION:Poster presentation by Rachel de Jong at IC2S2 conference \n\n\n\nAuthors: Rachel de Jong; Mark van der Loo; Frank Takes \n\n\n\nComplex networks are often used to describe the interactions between individuals or organizations within social or economic systems. In order to comply with regulations regarding privacy and data protection\, such data is frequently anonymized by leaving out personal identifiers of the nodes. However\, in such cases (properties of) a seemingly anonymized individual may still be re-identified based on its structural position in the network [1]. \n\n\n\nThis is particularly relevant for National Statistical Institutes (NSIs) that are applying network science to population scale social network data [2]. When releasing data for research purposes\, NSIs rely on statistical disclosure control (SDC) techniques for data protection [3]. A central concept in this field is anonymity: the number of equivalent data points in an anonymized data set. Anonymity is an essential component in assessing the risk of disclosure. \n\n\n\nThis work presents a method for measuring the disclosure probability of nodes in networks\, that takes as a parameter the amount of information an adversary has about a node’s surrounding structure. We also provide an in-depth analysis of node anonymity in three well-known complex network models. The proposed j-anonymity distributions can be used to measure disclosure probabilities\, which in turn can be used to assess disclosure risk. Numerical experiments reveal that many nodes are de-anonymized when their neighbourhood of radius 2 is known. URL:https://www.popnet.io/events/measuring-anonymity-in-complex-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:20210731T090000 DTEND;TZID=Europe/Amsterdam:20210731T091500 DTSTAMP:20260502T141509 CREATED:20210719T063152Z LAST-MODIFIED:20211029T100325Z UID:556-1627722000-1627722900@www.popnet.io SUMMARY:The emergence of hierarchy in spatial diffusion over the life-cycle of innovations DESCRIPTION:Conference talk at IC2S2 \n\n\n\nAuthors: Eszter Bokanyi\, Martin Novák\, Ákos Jakobi and Balazs Lengyel \n\n\n\nABSTRACTUsing a model capturing distance-decay\, urban scaling\, and hierarchical difference\, we show that hierarchical diffusion has an increasing role over the life cycle in the spatial adoption of an online social network. URL:https://www.popnet.io/events/the-emergence-of-hierarchy-in-spatial-diffusion-over-the-life-cycle-of-innovations/ 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:20210730T151000 DTEND;TZID=Europe/Amsterdam:20210730T155000 DTSTAMP:20260502T141509 CREATED:20210706T081542Z LAST-MODIFIED:20220112T135209Z UID:522-1627657800-1627660200@www.popnet.io SUMMARY:Population-scale Social Network Analysis DESCRIPTION:Keynote Frank Takes at IC2S2 2021 \n\n\n\nMissed the keynote by Frank Takes at IC2S2 2021? You can view the full presentation here. \n\n\n\nAbstract \n\n\n\nThe use of country-wide administrative register data enables the discovery of population-scale insights into contemporary problems such as segregation\, inequality and poverty. This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. I will discuss how the type of formal links in this social network require one to critically rethink network analysis concepts such as the unit of analysis\, measurement errors effects and the boundary specification problem. Moreover\, I will show how the analysis of a population-scale multilayer network of family\, work\, school\, household and neighborhood relations enables us to revisit the well-known small-world phenomenon from a unique angle. Finally\, I outline the possibilities of population-scale network data for various areas of social science research. \n\n\n\nAbout Frank Takes \n\n\n\nFrank Takes is head of the Computational Network Science Lab at Leiden University and research fellow in computational social science at the University of Amsterdam. He is co-director of the Platform for Population-scale Social Network Analysis (POPNET) and board member of the Dutch Network Science Society (NL NetSci). His research deals with methods for large-scale social network analysis\, with a focus on applications in economic networks\, scientific collaboration networks and population-scale social networks.  URL:https://www.popnet.io/events/population-scale-social-network-analysis/ 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:20210728T104500 DTEND;TZID=Europe/Amsterdam:20210728T110000 DTSTAMP:20260502T141509 CREATED:20210719T064642Z LAST-MODIFIED:20211029T100338Z UID:560-1627469100-1627470000@www.popnet.io SUMMARY:Income of home neighbourhood and the structure and concentration of online social ties in US metro areas DESCRIPTION:Conference talk at IC2S2 \n\n\n\nAuthors: Sándor Juhász\, Ádám Kovács\, Balázs Lengyel and Eszter Bokányi \n\n\n\nABSTRACTThis study shows that online social connections of people in poor neighbourhoods are more spatially concentrated and structurally cohesive than the network of people living in better-off areas of the top 50 metropolitan areas of the US. URL:https://www.popnet.io/events/income-of-home-neighbourhood-and-the-structure-and-concentration-of-online-social-ties-in-us-metro-areas/ 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:20210728T094500 DTEND;TZID=Europe/Amsterdam:20210728T100000 DTSTAMP:20260502T141509 CREATED:20210719T064140Z LAST-MODIFIED:20211029T100345Z UID:558-1627465500-1627466400@www.popnet.io SUMMARY:The effect of commuting on the income assortativity of social network ties DESCRIPTION:Conference talk at IC2S2 \n\n\n\nAuthors: Eszter Bokanyi\, Sándor Juhász\, Márton Karsai and Balazs Lengyel \n\n\n\nABSTRACTIn this work\, we investigate home-work locations and mutual followership ties of Twitter users from the top 50 metropolitan areas of the United States. We find that despite the heterogeneity of spatial structures in cities\, above median commuting reduces the income assortativity of social networks by 30% on average. URL:https://www.popnet.io/events/the-effect-of-commuting-on-the-income-assortativity-of-social-network-ties/ 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:20210709T161500 DTEND;TZID=Europe/Amsterdam:20210709T163000 DTSTAMP:20260502T141509 CREATED:20210707T061007Z LAST-MODIFIED:20211029T100351Z UID:529-1625847300-1625848200@www.popnet.io SUMMARY:The community structure of global scientific collaboration - lecture at Networks2021 conference DESCRIPTION:Lecture by Frank Takes on the Networks 2021 conference \n\n\n\nAuthors: Hanjo Boekhout; Eelke Heemskerk; Frank Takes \n\n\n\nIn this work we study the structure of global scientific collaboration\, in an attempt to better understand the internationalization of research. In particular\, we are interested in the existence of closely collaborating scientific communities. Indeed\, it is well-known that even though science knows no borders\, collaboration ties are not randomly created between individual scholars. Instead\, we anticipate geographical\, political and cultural factors to play a crucial role in this process. In this paper we set out to empirically investigate to what extent this is the case by providing a large-scale analysis of 23 million publications from Web of Science in the period 2008—2019. URL:https://www.popnet.io/events/the-community-structure-of-global-scientific-collaboration-lecture-at-networks2021-conference/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/07/vis-hcp10.png END:VEVENT BEGIN:VEVENT DTSTART;TZID=Europe/Amsterdam:20210706T204500 DTEND;TZID=Europe/Amsterdam:20210706T210000 DTSTAMP:20260502T141509 CREATED:20210702T072413Z LAST-MODIFIED:20211029T100401Z UID:519-1625604300-1625605200@www.popnet.io SUMMARY:Measuring Anonymity in Complex Networks at Networks 2021 conference DESCRIPTION:Lecture by Rachel de Jong at Networks2021 conference \n\n\n\nAuthors: Rachel de Jong; Mark van der Loo; Frank Takes \n\n\n\nComplex networks are often used to describe the interactions between individuals or organizations within social or economic systems. In order to comply with regulations regarding privacy and data protection\, such data is frequently anonymized by leaving out personal identifiers of the nodes. However\, in such cases (properties of) a seemingly anonymized individual may still be re-identified based on its structural position in the network [1]. \n\n\n\nThis is particularly relevant for National Statistical Institutes (NSIs) that are applying network science to population scale social network data [2]. When releasing data for research purposes\, NSIs rely on statistical disclosure control (SDC) techniques for data protection [3]. A central concept in this field is anonymity: the number of equivalent data points in an anonymized data set. Anonymity is an essential component in assessing the risk of disclosure. \n\n\n\nThis work presents a method for measuring the disclosure probability of nodes in networks\, that takes as a parameter the amount of information an adversary has about a node’s surrounding structure. We also provide an in-depth analysis of node anonymity in three well-known complex network models. The proposed j-anonymity distributions can be used to measure disclosure probabilities\, which in turn can be used to assess disclosure risk. Numerical experiments reveal that many nodes are de-anonymized when their neighbourhood of radius 2 is known. URL:https://www.popnet.io/events/10320-measuring-anonymity-in-complex-networks/ CATEGORIES:Conference talk ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2021/07/Networks-2021.png END:VEVENT END:VCALENDAR