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DTSTART;TZID=Europe/Amsterdam:20220713T152000
DTEND;TZID=Europe/Amsterdam:20220713T153000
DTSTAMP:20260504T013112
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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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220718T230000
DTEND;TZID=Europe/Amsterdam:20220722T225959
DTSTAMP:20260504T013112
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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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220720T192000
DTEND;TZID=Europe/Amsterdam:20220720T202000
DTSTAMP:20260504T013112
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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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220721T190000
DTEND;TZID=Europe/Amsterdam:20220721T200000
DTSTAMP:20260504T013112
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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