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DTSTART;TZID=Europe/Amsterdam:20220913T102300
DTEND;TZID=Europe/Amsterdam:20220913T104300
DTSTAMP:20260504T001849
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:20220913T104600
DTEND;TZID=Europe/Amsterdam:20220913T110600
DTSTAMP:20260504T001849
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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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220913T131600
DTEND;TZID=Europe/Amsterdam:20220913T133600
DTSTAMP:20260504T001849
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:20220913T133900
DTEND;TZID=Europe/Amsterdam:20220913T135900
DTSTAMP:20260504T001849
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:20220913T151600
DTEND;TZID=Europe/Amsterdam:20220913T153600
DTSTAMP:20260504T001849
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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