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DTSTART;TZID=Europe/Amsterdam:20221116T070000
DTEND;TZID=Europe/Amsterdam:20221116T160000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20221103T161500
DTEND;TZID=Europe/Amsterdam:20221103T171500
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20221103T160000
DTEND;TZID=Europe/Amsterdam:20221103T170000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20221027T110000
DTEND;TZID=Europe/Amsterdam:20221027T120000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20221021T114500
DTEND;TZID=Europe/Amsterdam:20221021T120000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220919T100000
DTEND;TZID=Europe/Amsterdam:20220919T110000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220913T151600
DTEND;TZID=Europe/Amsterdam:20220913T153600
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220913T133900
DTEND;TZID=Europe/Amsterdam:20220913T135900
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220913T131600
DTEND;TZID=Europe/Amsterdam:20220913T133600
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220913T104600
DTEND;TZID=Europe/Amsterdam:20220913T110600
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220913T102300
DTEND;TZID=Europe/Amsterdam:20220913T104300
DTSTAMP:20260503T135658
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
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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220721T190000
DTEND;TZID=Europe/Amsterdam:20220721T200000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220720T192000
DTEND;TZID=Europe/Amsterdam:20220720T202000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220718T230000
DTEND;TZID=Europe/Amsterdam:20220722T225959
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220713T152000
DTEND;TZID=Europe/Amsterdam:20220713T153000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220616T130000
DTEND;TZID=Europe/Amsterdam:20220616T140000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220519T083000
DTEND;TZID=Europe/Amsterdam:20220519T160000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220517T090000
DTEND;TZID=Europe/Amsterdam:20220517T100000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220512T133000
DTEND;TZID=Europe/Amsterdam:20220512T150000
DTSTAMP:20260503T135658
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
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220510T090000
DTEND;TZID=Europe/Amsterdam:20220510T100000
DTSTAMP:20260503T135658
CREATED:20220421T120534Z
LAST-MODIFIED:20220421T121833Z
UID:801-1652173200-1652176800@www.popnet.io
SUMMARY:POPNET Connects with Naja Hulvej Rod
DESCRIPTION:Please register for the seminar via the button. You will receive a link to the virtual meeting via email.  \n\n\n\n\n\nSocial phenomena and health: exploring the role of networks and group dynamics\n\n\n\nReducing health inequalities is a major public health priority\, but fundamental questions such as how multifaceted and socially patterned diseases like type 2 diabetes\, respiratory diseases and mental disorders behave in populations remain unanswered. To address these questions\, health science needs to evolve from primarily focusing on individual exposures and single diseases to a system-oriented approach\, considering the dynamics between diseases and events at an individual level\, and the subsequent group dynamics at a population level. This talk will present empirical results on social phenomena such as social networks\, workplace social capital and childhood adversity in families which arise from group dynamics and relate them to population health.  \n\n\n\n\n\n\n\nAbout Naja Hulvej Rod\n\n\n\nNaja Hulvej Rod is Professor of Epidemiology and Chair of the Section of Epidemiology\, University of Copenhagen. She is leading the Complexity and Big Data Group\, which aim at studying the social and biological factors determining health and disease across the life span. She has extensive expertise in working with longitudinal datasets\, register-based research and complex modelling including social influences and group dynamics. To embrace complexity in epidemiology\, she actively explores new sources (e.g. smartphones and geocoding) of ‘big data’\, incorporate system thinking and leverage insights across disciplines\, and she has been involved in several citizen science projects with a direct societal engagement and impact. Naja Hulvej Rod is PI of the Danish Life Course Cohort (DANLIFE) Study\, the Well-being in Hospital Employee Cohort (WHALE) study\, the SmartSleep program\, and the Corona Minds project.
URL:https://www.popnet.io/events/popnet-connects-with-naja-hulvej-rod/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220503T150000
DTEND;TZID=Europe/Amsterdam:20220503T190000
DTSTAMP:20260503T135658
CREATED:20220405T195259Z
LAST-MODIFIED:20220421T124539Z
UID:783-1651590000-1651604400@www.popnet.io
SUMMARY:How anonymous are you?!
DESCRIPTION:Statistics Netherlands (CBS) has enormous amounts of data at its disposal\, from which social networks are also derived. But how anonymous are you in this data? Is it possible for hackers to extract sensitive information from these data sets\, by combining them with other data sources? \n\n\n\nIn the hackathon ‘From Person to Open Data’\, you\, as a ‘potential attacker’\, will search for network relations of a given list of people. If you are able to find the most relations within 4 hours\, you receive a prize! \n\n\n\nInterested? Sign up for free before April 27th! The link to the registration form is available on Brightspace\, or request the registration link via popnet@uva.nl.Pizza and drinks at the end of the day are included\, and during the hackathon\, we will provide beverages and snacks. \n\n\n\nTimeProgramLocation14:45Walk-inRoom 40515:00IntroductionRoom 40515:10AssignmentRoom 302-30419:00End of assignmentPizza and drinksFooBar19:30Prize ceremony FooBar\n\n\n\nFor whom?Students. You don’t have to speak Dutch\, but it can be advantageous\, since the data is about Dutch people. You can sign up as a team of two\, but it is also possible to sign up individually. If you want\, we will pair you up with another student on the day itself. \n\n\n\nLocationLeiden University\, Snellius buildingNiels Bohrweg 1\, 2333 CA Leiden \n\n\n\nOrganisationThis hackathon is organized by the Anonymity in Complex Networks project (ANO-NET)\, the Methodology department of Statistics Netherlands (CBS)\, the Population-scale Social Network Analysis (POPNET) project of the University of Amsterdam and Leiden University and the Computational Network Science (CNS) research group of Leiden University.
URL:https://www.popnet.io/events/how-anonymous-are-you/
CATEGORIES:Hackathon
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220429T160000
DTEND;TZID=Europe/Amsterdam:20220429T170000
DTSTAMP:20260503T135658
CREATED:20220419T093023Z
LAST-MODIFIED:20220421T121550Z
UID:798-1651248000-1651251600@www.popnet.io
SUMMARY:Population-scale Social Network Analysis
DESCRIPTION:Lecture by co-director Frank Takes for the Maths and Statistics Department in the University of Limerick. \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-4/
CATEGORIES:Lecture
ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2022/04/UL-logo.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220422T150000
DTEND;TZID=Europe/Amsterdam:20220422T160000
DTSTAMP:20260503T135658
CREATED:20220419T091322Z
LAST-MODIFIED:20220421T120822Z
UID:795-1650639600-1650643200@www.popnet.io
SUMMARY:LCN2 seminar: The anatomy of a population-scale social network
DESCRIPTION:Lecture for Leiden Complex Networks Network (LCN2) by Eszter Bokányi.Title: The anatomy of a population-scale social network \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/lcn2-seminar-the-anatomy-of-a-population-scale-social-network/
CATEGORIES:Lecture
ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/04/LCN2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220421T151500
DTEND;TZID=Europe/Amsterdam:20220421T161500
DTSTAMP:20260503T135658
CREATED:20220419T090635Z
LAST-MODIFIED:20220421T120716Z
UID:792-1650554100-1650557700@www.popnet.io
SUMMARY:Research Colloquium on Business Informatics
DESCRIPTION:As part of the Research Colloquium on Information Systems and Data Science of the Institute of Information Systems of Leuphana University Lüneburg\, co-director Frank Takes will speak on “Population-scale Social Network Analysis” via Zoom. \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 analysis 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/research-colloquium-on-business-informatics/
CATEGORIES:Lecture
ATTACH;FMTTYPE=image/jpeg:https://www.popnet.io/wp-content/uploads/2022/04/Leuphana.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220419T090000
DTEND;TZID=Europe/Amsterdam:20220419T100000
DTSTAMP:20260503T135658
CREATED:20220401T083134Z
LAST-MODIFIED:20220401T083825Z
UID:768-1650358800-1650362400@www.popnet.io
SUMMARY:POPNET Connects with Rense Corten
DESCRIPTION:Please register for the seminar via the button. You will receive a link to the virtual meeting via email.    \n\n\n\n\nRegister\n\n\n\n\nSocial networks research with digital traces data\n\n\n\nThe emergence of the internet and its various modes of online interaction have created unprecedented opportunities for social scientists to study classic social questions in new ways\, but also to ask new questions. This holds in particular for themes like social networks\, social order\, and cooperation. In this talk I will present various examples of my work over the past years on these themes\, drawing on a variety of different “digital traces” data sources\, including social media and online markets. \n\n\n\nAbout Rense Corten\n\n\n\n\n\n\n\nRense Corten is Associate Professor at the Department of Sociology. His research revolves around the themes of cooperation\, trust\, and (the dynamics of) social networks\, with empirical applications including adolescent networks\, social media\, the sharing economy\, online criminal networks\, and laboratory experiments. In 2016 he received an NWO Vidi grant for a research project on the origins and consequences of trust in the sharing economy.He obtained his PhD in social sciences in 2009 and his doctorate in sociology in 2004 at Utrecht University.
URL:https://www.popnet.io/events/popnet-connects-with-rense-corten/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220405T090000
DTEND;TZID=Europe/Amsterdam:20220405T100000
DTSTAMP:20260503T135658
CREATED:20220329T093530Z
LAST-MODIFIED:20220401T083859Z
UID:757-1649149200-1649152800@www.popnet.io
SUMMARY:POPNET Connects with Willem Boterman
DESCRIPTION:Please register for the seminar via the button. You will receive a link to the virtual meeting via email.   \n\n\n\n\nRegister\n\n\n\n\nSchool choice and school segregation\n\n\n\nSchool segregation is both a result and a cause of educational inequalities in societies world-wide. Understanding mechanisms for the emergence of school segregation is crucial for understanding potential policy solutions. A vast literature has identified a number of main factors in school segregation of which residential patterns and school choice are arguably the most important. Drawing on findings from a range of qualitative and quantitative studies\, I will outline the complexity of school segregation and suggest to combine existing approaches with a complexity science perspective. I will present both empirical evidence from the Netherlands and insights from theoretical models. \n\n\n\nAbout Willem Boterman\n\n\n\n\n\n\n\nWillem Boterman is Associate Professor Urban Geography at the University of Amsterdam. He combines qualitative and quantitative methods in his interdisciplinary work into the relationship between spatial and social inequalities. His work is primarily concerned with segregation in neighborhoods and schools\, but also with formations of social class and gender.
URL:https://www.popnet.io/events/popnet-connects-with-willem-boterman/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220322T090000
DTEND;TZID=Europe/Amsterdam:20220322T100000
DTSTAMP:20260503T135658
CREATED:20220304T122239Z
LAST-MODIFIED:20220304T122414Z
UID:753-1647939600-1647943200@www.popnet.io
SUMMARY:POPNET Connects with Marjolijn Das
DESCRIPTION:Please register for the seminar via the button. You will receive a link to the virtual meeting via email.  \n\n\n\n\nRegister\n\n\n\n\nUsing a whole population network in the social sciences\n\n\n\nNetwork research can have enormous added value in different substantive research fields\, ranging from epidemiology and infectious disease control to economics and the social sciences. This talk focuses on the use of integral administrative register data within the social sciences\, in particular the Dutch whole population network which contains links between neighbours\, household members\, family\, colleagues and classmates. I will present social research done at CBS with register data and with the network\, such as contagion of demographic behaviour\, segregation\, and clustering of crime within networks. I will also sketch avenues for future social research. \n\n\n\n\n\n\n\nAbout Marjolijn Das\n\n\n\nMarjolijn Das works as a senior statistical researcher at Statistics Netherlands and is an endowed professor of Urban Statistics at the Erasmus School of Social and Behavioural Sciences\, Erasmus University Rotterdam\, appointed within the Leiden-Delft-Erasmus Centre for BOLD Cities. Her research focuses on quantitative social research with large-scale register data. For a number of years\, she has been working with the whole population network derived by CBS. Her research theme is the interplay between people and their urban/social environment. She published on spatial inequalities\, mobility in the life course\, social and family networks and the intergenerational transmission of education. She holds a PhD in Ethology from Utrecht University.
URL:https://www.popnet.io/events/popnet-connects-with-marjolijn-das/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220221T100000
DTEND;TZID=Europe/Amsterdam:20220221T110000
DTSTAMP:20260503T135658
CREATED:20220216T150605Z
LAST-MODIFIED:20220216T150812Z
UID:748-1645437600-1645441200@www.popnet.io
SUMMARY:POPNET Connects with Fariba Karimi
DESCRIPTION:Please register for the seminar via the button. You will receive a link to the virtual meeting via email.    \n\n\n\n\nRegister\n\n\n\n\nNetwork Inequality: Emergence of inequalities and bias in social networks\n\n\n\nIn this talk\, I show how fundamental properties of social interactions such as homophily can result in the emergence of inequalities and biases in society and algorithms and what societal consequences it has on the visibility of minorities. \n\n\n\n\n\n\n\nAbout Fariba Karimi\n\n\n\nFariba Karimi is leading the Network Inequality group at Complexity Science Hub. \n\n\n\nHer expertise encompasses network analysis\, computational social science\, data science\, and agent-based modeling. Her current research focuses on emergence of inequalities and biases in social networks and online algorithms. She has recently awarded a Digital Humanism grant to study the impact of algorithms on exacerbating social inequalities. \n\n\n\nHer research appears in leading journals including Nature Human Behavior\, Scientific Reports\, Nature Humanities and Social Sciences Communications\, Advances in Complex Systems\, and EPJ Data Science. She is among the 7 candidates for the Hedy Lamarr Prize of the city of Vienna honoring women researchers in Austria for their outstanding achievements in the field of information technology.
URL:https://www.popnet.io/events/popnet-connects-with-fariba-karimi/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220208T100000
DTEND;TZID=Europe/Amsterdam:20220208T110000
DTSTAMP:20260503T135658
CREATED:20220112T124725Z
LAST-MODIFIED:20220113T123313Z
UID:708-1644314400-1644318000@www.popnet.io
SUMMARY:POPNET Connects with David Schoch
DESCRIPTION:Please register for the seminar via the button. You will receive a link to the virtual meeting via email.   \n\n\n\n\nRegister\n\n\n\n\nRethinking one-mode projections\n\n\n\nTwo-mode networks are usually analyzed in one of two ways. With the “direct” approach using methods tailored for bipartite graphs\, or with the “conversion” approach\, which includes all methods that project the two-mode network onto each mode separately.In this talk\, I focus on one-mode projections where one mode serves as the primary mode and the second mode only as a proxy for relations among actors in the primary mode. Drawing parallels to item response theory\, I argue that projected (and dichotomized) ties are conceptually different than traditional ties\, which therefore restricts the applicability and interpretability of standard network analytic tools in such cases. I will introduce a set of alternative methods to analyze one-mode projections and exemplify these with several empirical examples. \n\n\n\nAbout David Schoch\n\n\n\n\n\n\n\nDavid came to Manchester in September 2018 as a Presidential Fellow in Sociology. He received his PhD in 2015 at the Department of Computer and Information Science\, University of Konstanz\, Germany. During that time\, He was also a member of the Graduate School of Decision Sciences. His thesis focused on theoretical advancements for network centrality in the field of social network analysis. He continued as a postdoctoral researcher at the University of Konstanz (11/2015-10/2017) and ETH Zurich (11/2017-8/2018). David also holds a diploma in economathematics from the Karlsruhe Institute of Technology\, Germany.In his current research\, he focuses on methodological and theoretical contributions to the field of Social Network Analysis. Additionally\, he is involved in a project on disinformation campaigns on social media platforms (“political astroturfing”).
URL:https://www.popnet.io/events/popnet-connects-with-david-schoch/
CATEGORIES:Seminar
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END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220121T150000
DTEND;TZID=Europe/Amsterdam:20220121T160000
DTSTAMP:20260503T135658
CREATED:20220117T111449Z
LAST-MODIFIED:20220117T111509Z
UID:725-1642777200-1642780800@www.popnet.io
SUMMARY:Population-scale Social Network Analysis
DESCRIPTION:Seminar at GRAFO – GRUP DE RECERCA EN ANTROPOLOGIA FONAMENTAL I ORIENTADA (University of Barcelona) by Frank Takes and Yuliia Kazmina \n\n\n\nCountry-wide administrative register data\, as studied within the POPNET Project\, enables the discovery of population-scale insights into contemporary social scientific problems such as segregation\, inequality\, loneliness\, and poverty. This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. We discuss how the 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\, but also allows us to in a unique way revisit the well-known small-world phenomenon. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population as well as emerging socio-economic inequality and segregation patters.
URL:https://www.popnet.io/events/population-scale-social-network-analysis-3/
CATEGORIES:Lecture
ATTACH;FMTTYPE=image/png:https://www.popnet.io/wp-content/uploads/2022/01/Grafo-logo.png
END:VEVENT
END:VCALENDAR