POPNET

The Closing Window? Network Determinants of Economic Prosperity in a Longitudinal Population-scale Social Network
2024-10-18 10:00 - 19:00 (Europe/Amsterdam)
On October 18, Yuliia Kazmina will present her work on “The Closing Window? Network Determinants of Economic Prosperity in a Longitudinal Population-scale Social Network” at the Dutch NetSci Symposium, which will take place at Eindhoven University of Technology.

Abstract

This work presents a comprehensive longitudinal exploration of trends of socio- economic segregation and inequalities across a span of a recent decade, aiming to reveal their impact on the prospects for upward mobility.

Utilizing population-scale social network register data on all residents of the Netherlands, the research delves into the shifting patterns of income and wealth distributions, offering an in-depth overview of their longitudinal trends and interplay. We juxtapose the evolving dynamics of income and wealth accumulation against a specific aspect of social network structure: segregation patterns along the socio-economic status dimension. To unravel the drivers behind observed macro-trends of segregation and inequality, we distinguish various relevant subgroups within the population as defined by their socio-economic background and social network position. Through comparative analysis across diverse socio-economic strata, we identify the key social network determinants contributing to individuals’ economic prosperity and explore how these influences vary across the spectrum of income and wealth distribution.

The study leverages register data from the Netherlands covering the country’s whole population and providing information on one’s socio-economic standing as well as comprehensive mapping of social networks. The relational aspect of register data sheds light on formal links such as kinship, neighbors, classmates, colleagues, and household members. Methodologically, the study employs a mix of supervised and unsupervised machine learning to analyze longitudinal patterns of segregation and economic inequalities. We first employ clustering analysis to define common trajectories of economic development expressed through a combination of income change as well as accumulation of wealth and debt. We then relate these trajectories to the socioeconomic network composition of these individuals with a particular focus on income and wealth assortativity as well as average income and wealth distance to alters while controlling for the size of the social network.

In contrast to recent work on social capital by Chetty et al., we contend that the key factor for improving economic performance among the lower end of the prosperity distribution isn’t merely low segregation and high economic connectedness. Rather, it lies in having a significant proportion of more affluent contacts, while ensuring that the prosperity gap is within an optimal range. While having somewhat wealthier contacts can be advantageous, once the prosperity gap becomes too wide, its predictive power may diminish or even have a negative impact. Therefore, we anticipate a concave relationship between the positive prosperity gap with alters and enhanced economic performance,especially for those at the lower end of the prosperity distribution.

Our contribution lies not only in comprehensively mapping dynamics of segregation vs economic inequalities on the country level but also in providing micro-analysis of the drivers that contribute to the observed macro outcomes. This interdisciplinary approach enables a comprehensive perspective on how individual pathways to economic successare embedded within a broader structural framework.

References1. Chetty, R., Jackson, M.O., Kuchler, T. et al. Social capital I: measurement and associationswith economic mobility. Nature 608, 108–121 (2022). https://doi.org/10.1038/s41586-022-04996-42. Chetty, R., Jackson, M.O., Kuchler, T. et al. Social capital II: determinants of economicconnectedness. Nature 608, 122–134 (2022). https://doi.org/10.1038/s41586-022-04997-3

Increasing mobility is linked to decreasing cohesion of personal networks overthe lifecourse of an entire population
2024-10-18 10:00 - 19:00
On October 18, Eszter Bokányi will present her work on “Increasing mobility is linked to decreasing cohesion of personal networks over the lifecourse of an entire population” at the Dutch NetSci Symposium, which will take place at Eindhoven University of Technology.

Abstract

Growing accessibility between places and a changing society foster higher human mobility both daily and long-term, which results in an increased social connectivity between faraway places. Thus, geographic displacement creates new connections and at the same time rearranges the existing spatial structure of social networks. Both of these mechanisms are little understood in the existing literature of social network formation processes. Moreover, up until now, it has also been a challenge to systematically follow the temporal evolution of an entire population’s social network structure.

In this work, we use a unique longitudinal population-scale network dataset sourced from Statistics Netherlands. This network contains family, work, school, household, and next-door neighbor connections derived from administrative registers, that together constitute a multilayer social opportunity structure for all residents of the Netherlands between 2009 and 2022. We follow the patterns of individuals’ network surroundings over time, and measure size, closure, and geographical dispersion of ego networks. Size is captured by degree, closure by excess closure [1], which is based on the node clustering coefficient. Geographical dispersion is given by the average distance from network neighbors, and the average share of network neighbors in the same municipality or the same next-door neighborhood as the ego.

The three findings presented in this work are that while the average size of ego networks stays stable over the observed period, average closure drops by as much as 10%, and the average distance from network neighbors grows, while the average share of network neighbors in the same geographic area decreases. We use multivariate regressions to show that the observed decrease in the closure is significantly linked to the growing geographic dispersion, thus, the increasing daily and long-term mobility of people. We control for demographic and socio-economic background including the age, migrant generation, income, and whether people attend school and have employment. The regressions confirm that beyond degree and demographics, variables that capture people’s mobility are linked to the opening up of individual networks.

This work is the first of its kind that aims to map the temporal network of an entire population structure comprehensively. As such, it offers a starting point for a wide variety of impactful network science research at the level of a complete population.

[1] Bokányi, E., Heemskerk, E. M., & Takes, F. W. (2023). The anatomy of a population-scale social network. Scientific Reports, 13(1), 9209.

Contacts in contexts: A population-scale social network approach to the study of close intergroup social ties
2024-06-28 17:20 - 19:00
On June 28, Nicolás Soler, PhD Candidate (Erasmus University Rotterdam) and POPNET Fellow, will give a lecture about a new approach that investigates social context in intergroup social ties across multiple contexts.

Abstract

Close intergroup social ties provide fertile ground to cultivate mutual trust and tolerance. Social context shapes the opportunities to form such ties, but what is exactly the relationship between opportunities and realised intergroup social ties? When assessing the importance of social context, previous quantitative research has faced a trade-off between generalisability and granularity in data collection. The challenge is to study the relevance of social context in intergroup social ties across multiple contexts, in detail, and at a large scale. To do so, we map social context using newly available population-scale social network data from the Netherlands. We link them to survey data on close intergroup social ties. We showcase our novel approach by investigating which contexts sustain close social ties between immigrants and natives, i.e. individuals born and residing in the Netherlands. Our results confirm that natives who have more opportunities to interact with immigrants are more likely to have a close intergroup social tie with them. We find that this effect is stronger when the opportunities for intergroup interaction arise in private rather than public contexts. We also find that natives with at least one immigrant parent are important brokers in this type of intergroup social ties.

Co-authored with Eelke and Yuliia. Presented as poster presentation.

Parenthood status of siblings, half-siblings and cousins and entry into parenthood. A horizontal kinship network approach
2024-06-27 08:30 - 10:10
On June 27, Vera de Bel, Alyona Artamonova, Takayuki Hiraoka, Mirkka Danielsbacka, Antti Tanskanen, Marijtje van Duijn will present their work on “Parenthood status of siblings, half-siblings and cousins and entry into parenthood. A horizontal kinship network approach” at Sunbelt 2024, which will take place in at Heriot-Watt University, Edinburgh.

Abstract

Horizontal kinship ties last a lifetime, and life-course transitions of horizontal kin are known to be interconnected. Prior research, with a focus on siblings, has demonstrated their influence on various life events, including home leaving (Her, Vergauwen, & Mortelmans, 2022), marriage and divorce behavior (Buyukkececi & Leopold, 2021), and fertility (Buyukkececi & Leopold, 2021; Lyngstad & Prskawetz, 2010). However, siblings are just one example of individuals’ horizontal kin, and the impact extends to half-siblings and cousins, shaping both individual trajectories and those of their relatives. Moreover, the influence of horizontal kin may vary based on factors such as the degree of relatedness (full siblings, half-siblings, or cousins), kin’s gender ((half-)brothers, (half-)sisters, male or female cousins), and lineage (paternal or maternal side of the family). This study seeks to analyze the complete horizontal kinship network to understand how individuals’ entry into parenthood is associated with the parenthood status of their horizontal kin. More specifically, the study examines whether 1) close kin compared to more distant kin, 2) female compared to male kin, and 3) maternal compared to paternal kin have a stronger influence on an individual’s entry into parenthood. Utilizing unique registry data that documents the kinship networks of the entire Dutch (van der Laan, de Jonge, Das, Te Riele, & Emery, 2022) and Finnish population, the study will employ a multi-level model considering the nested structure and dependence of relationships (Snijders & Bosker, 2012; van Duijn, 2013). First results and a comparison between the Dutch and Finnish contexts will be presented.

POPNET presents their work during Sunbelt Conference 2024
2024-06-26 00:00 - 2024-06-29 23:59
POPNET proudly joins the 44th edition of the Sunbelt Conference in Edinburgh from 24th June to June 30th 2024. The Sunbelt Conference is part of the International Network for Social Network Analysis (INSNA), providing an interdisciplinary venue for social scientists, mathematicians, computer scientists, ethnologists, and others to present current work in the area of social networks.

Exploring Social Network Analysis

In alignment with POPNET’s aim to unlock longitudinal social network data on the Dutch population, this year theme of the Sunbelt 2024 is ‘Networks and Resilience’. Throughout the conference, various members of POPNET will give talks in the field of social network analysis. Social capital, network clustering, and family networks are just a few examples of the topics that will be covered at Sunbelt 2024.

Find below a list of the titles they will present during the conference:

26 June, 14:30: “Is Social Capital Good for you?”, by Heemskerk, Eelke; Takes, Frank W. .

26 June, 14:30: “Social Network Determinants of Economic Prosperity in a Longitudinal Population-scale Social Network”, by Kazmina, Yuliia; Heemskerk, Eelke; Bokanyi, Eszter; Takes, Frank W. .

27 June, 8:30: “Parenthood status of siblings, half-siblings and cousins and entry into parenthood. A horizontal kinship network approach”, by de Bel, Vera; Artamonova, Alyona; Hiraoka, Takayuki; Danielsbacka, Mirkka; Tanskanen, Antti; van Duijn, Marijtje.

27 June, 10:40: “Large and small-scale dynamics of a longitudinal population-scale social network”, by Bokanyi, Eszter; Kazmina, Yuliia; van der Kooij, Emilia; Takes, Frank; Heemskerk, Eelke.

29 June, 14:00: “Connectivity and community structure of online and register-based population-scale social networks”, by Menyhért, Márton; Bokányi, Eszter; Corten, Rens; Heemskerk, Eelke; Kazmina, Yuliia; Takes, Frank W. .

Promise and perils of population-scale social network analysis
2023-10-26 15:30 - 17:00
On Thursday 26 October, POPNET PI Frank Takes will present his work online for the IAS lecture series of the Institute for Analytical Sociology in Norrköping, Sweden.

Abstract

A relatively recently emerged line of research is dedicated to harnessing large-scale population register data to address enduring questions within the realm of social science. In this presentation, we will specifically delve into the network dimension of such data, with a focus on data from the POPNET project, covering over 17 million people (i.e., the entirety of the Netherlands) and their ca. 800M family, household , school, work and next-door neighbor connections. We highlight the potential inherently present in this type of curated and comprehensive social network data through illustrative examples of results related to topics such as social capital, segregation, and migration. Furthermore, we will explore several methodological considerations and challenges related to under- and oversampling of individual connections through opportunity structures, including findings on the veracity of skewed degree distributions in the real world. We also highlight network-structure related concerns w.r.t. privacy, in particular disclosure risk and more in general the anonymization of network data for scientific research.

Node anonymity in networks: The infectiousness of uniqueness
2023-06-29 21:30 - 22:50
On Thursday 29 June, POPNET PhD candidate Rachel de Jong will present her work on “Node anonymity in networks: The infectiousness of uniqueness” at Sunbelt 2023, which will take place in Portland, Oregon.

Social-economic segregation in a Population-Scale Social Network
2023-06-29 18:40 - 20:00
On Thursday 29 June, POPNET PhD candidate Yuliia Kazmina will present her work on “Social-economic segregation in a Population-Scale Social Network” at Sunbelt 2023, which will take place in Portland, Oregon.

The small-world structure of a population-scale social network
2023-06-29 18:40 - 20:00
On Thursday 29 June, POPNET PI Frank Takes will present his work on “The small-world structure of a population-scale social network” at Sunbelt 2023, which will take place in Portland, Oregon.

The anatomy of a population-scale social network
2023-06-29 18:40 - 20:00
On Thursday 29 June, POPNET Postdoctoral Researcher Eszter Bokányi will present her work on “The anatomy of a population-scale social network” at Sunbelt 2023, which will take place in Portland, Oregon.

POPNET Connects with Bas Hofstra
2023-05-22 11:00 - 12:00
This seminar is hybrid. Please register to attend the seminar online or on-site via the button. You will receive a link to the virtual meeting via email or a confirmation containing the on-site location

Academic Migration: Interdisciplinary Hierarchy, Closure, or Similarity?

The last two decades have seen a surge in research initiatives in many scientific fields surrounding interdisciplinarity. This has spawned many interdisciplinary research centers on US university campuses supported by billions of raised university or federal grant money to educate students as well as to facilitate interdisciplinary exchange and collaborations between faculty. This is often led by the belief that interdisciplinary exchange in science pushes research fields forward and accelerates breakthrough discovery. Interdisciplinary scientific collaboration is argued to pull together diverse insights from multiple bodies of knowledge, is unrestricted by disciplinary boundaries or semantics, and can draw from a larger methodological tool set.

Yet in spite of the push for interdisciplinary exchanges, there is surprisingly little empirical knowledge on its prevalence, patterning, or determinants. This is remarkable, because prior work shows that the social fabric of science itself is inherently shaped by exchanges of ideas, knowledge, and scholars themselves. Here, we focus on the latter and study scholarly hiring between disciplines. How prevalent are such exchanges between disciplines and how did this develop over time? And, more importantly, what determines the emergence of these interdisciplinary exchange structures: do some disciplines disproportionately place faculty in other disciplines, do disciplines cluster in such a hiring network, or are interdisciplinary hires mostly explained by how intellectually similar discipline are? These questions motivate the main goals of this study: identifying patterns of interdisciplinary exchange and explaining the emergence and persistence of these network structures.

We build on and extend prior work, particular the branches of literature on interdepartmental faculty hiring. Hiring often involves exchange indicative of implicit judgment; when one department hires a graduate student of another as faculty, there is a positive assessment of the graduate department that places the student. The assortment of these dyadic exchanges across disciplines represent migration networks of scholars that illuminate disciplinary hierarchy, clustering, and similarity. Our study empirically considers these network dynamics, thus providing insight into which disciplines wield the most influence in knowledge and scholarly exchange and why.

Our empirical site contains a realized scientific migration market of approximately 1.03 million records of nearly all US PhD students and corresponding metadata – names, supervisors, disciplines, and so forth – from their PhD theses (1980-2010). These data capture a wide cross-section of scholarly disciplines (N = 51) PhD-granting universities (N = 221), and departments (N = 8,205). What is particularly useful about this database is that it allows us to follow PhD recipients through time in a near-closed system of PhD recipients and their scholarly careers moving onward, thus showing the interdisciplinarization – which disciplines place students where and why? – of US academia. We analyze these interdisciplinary exchanges through a series of stERGMs that include nodal (discipline size, popularity), dyadic (natural language processing measures of intellectual distance between disciplines, field homophily), and closure dynamics.

About Bas Hofstra

Bas Hofstra is Assistant Professor at Radboud University’s Department of Sociology. His work orbits the study of diversity, stratification, and innovation. It captures longitudinal systems of social and cultural exchange: from the gestation and birth of networks, careers, ideas, or innovations, to their use, up until their eventual cessation. As such, his work strives for three interrelated goals: (i) answering substantive questions on causes and effects of social networks, while (ii) contributing to social theory, and (iii) using computational methods and big data. His research appeared (among others) in PNAS, American Sociological Review, Social Forces, Social Networks, and Nature Human Behaviour, and was honored with several grants and awards.

Segregation in population scale social networks
2023-05-16 10:00 - 11:00
Lecture by Eelke Heemskerk and Yuliia Kazmina at the Sociology Department of Utrecht University

We propose a social network-aware approach to study socio-economic segregation. The key question is whether patterns of segregation are more pronounced in social networks than the common spatial manifestations of segregation. We conduct a population-scale social network analysis to uncover socio-economic segregation at a comprehensive and highly granular level. At the basis of this analysis is high quality register data consisting of complete information on $sim$17.2 million registered residents of the Netherlands that are connected through 1.3 billion ties distributed over four distinct tie types. By comparing income assortativity between the social network and the spatial perspective, we find that the social network structure exhibits a factor of two higher segregation. This may signal that while at a particular scale of spatial aggregation (e.g., the geographical neighborhood), patterns of socio-economic segregation appear to be minimal, they in fact persist in the underlying social network structure. Furthermore, we discover higher socioeconomic segregation in larger cities as opposed to a widespread view of cities as hubs for diverse socioeconomic mixing. A population scale social network perspective hence offers a way to uncover hitherto “hidden” segregation that extends beyond spatial neighborhoods and infiltrates multiple aspects of human life.

POPNET Connects with Miranda Lubbers and Michał Bojanowski
2023-05-08 10:00 - 11:00
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Simulating society-wide networks based on NSUM

In a quest to understand larger patterns of sociability in a society and in particular its cohesion, different methods have been employed based on survey data, register data, and social media data. In this talk, we will discuss the ERC Advanced Grant project PATCHWORK, which intends to simulate society-wide networks based on the Network Scale-Up Method (NSUM) that will be implemented in a cross-national survey. We will discuss its unique features, how it compares to other methods used so far, and potential benefits of integrating different methods of simulating society-wide methods.

About Miranda Lubbers

Miranda Lubbers (PhD from Groningen University, the Netherlands) is Professor in Social and Cultural Anthropology of the Autonomous University of Barcelona (UAB), Spain, director of the COALESCE Lab, and an ICREA Acadèmia fellow. Her research studies how social networks shape processes of social cohesion, polarization, and exclusion.

About Michał Bojanowski

Michał Bojanowski is a computational sociologist (PhD from Utrecht University, the Netherlands), researcher and Assistant Professor, and R developer and trainer. His main research interests focus on (the dynamics of) social networks and mathematical/computational social science as tools for understanding conflict and cooperation. He is a researcher at the COALESCE Lab, Universitat Autònoma de Barcelona, participating in the Patchwork project, and an Assistant professor at the Chair of Quantitative Methods and Information Technology, Kozminski University.

POPNET Connects with Gert Stulp
2023-02-20 10:00 - 11:00
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Collecting personal networks to study social influences on fertility behaviour

People’s social environment is key in explaining their behaviour and preferences, including how many children people want and have. Unfortunately, obtaining information on the social environment, for instance through collecting personal network data, is difficult and often burdensome to participants. In this talk, I will first report on my experiences with collecting large personal network data (25 alters) from a representative sample of Dutch women. I’ll discuss the trade-offs researchers face when designing network studies between the burden to respondents and the reliability of characteristics of networks. Data quality is further discussed, including the usefulness of the concept “friend”. In the second part of the talk, I use these data to test evolutionary and sociological ideas about the breakdown of kin networks in contemporary populations as an explanation for the fertility decline. I’ll further discuss how personal network data can inform on social influences on fertility behaviour.

About Gert Stulp

Gert Stulp is based at the department of Sociology at the University of Groningen. He studies causes of the variation in the number of children people have and would like to have, and employs diverse methods in his research including personal network data collection, simulation studies, and machine learning.

POPNET Connects with Ozan Candogan
2023-02-06 14:00 - 15:00
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Controlling Epidemic Spread: Reducing Economic Losses with Targeted Closures

Data on population movements can be helpful in designing targeted policy responses to curb epidemic spread. However, it is not clear how to exactly leverage such data and how valuable they might be for the control of epidemics. To explore these questions, we study a spatial epidemic model that explicitly accounts for population movements and propose an optimization framework for obtaining targeted policies that restrict economic activity in different neighborhoods of a city at different levels. We focus on COVID-19 and calibrate our model using the mobile phone data that capture individuals’ movements within New York City (NYC). We use these data to illustrate that targeting can allow for substantially higher employment levels than uniform (city-wide) policies when applied to reduce infections across a region of focus. In our NYC example (which focuses on the control of the disease in April 2020), our main model illustrates that appropriate targeting achieves a reduction in infections in all neighborhoods while resuming 23.1%–42.4% of the baseline nonteleworkable employment level. By contrast, uniform restriction policies that achieve the same policy goal permit 3.92–6.25 times less nonteleworkable employment. Our optimization framework demonstrates the potential of targeting to limit the economic costs of unemployment while curbing the spread of an epidemic.

About Ozan Candogan

Ozan Candogan is a Professor of Operations Management at Chicago Booth. Prior to joining Booth, he was an Assistant Professor at the Fuqua School of Business where he was a member of the Decision Sciences area. He received his Ph.D. and M.S. degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology.Candogan’s main research area is social and economic networks. His research covers two complementary themes. On one hand, he investigates the impact of networks on operational decisions: He studies how to leverage network data (such as data on social networks, mobility networks, and trading networks) to improve operational decisions (ranging from pricing to inventory management and from information disclosure to facility location), and sheds light on the value of such data in different operational settings. On the other hand, he develops novel approaches and tools for the analysis of complex social and economic systems; and explores their applications to characterization of equilibria and dynamics in games, study of equilibria and comparative statics in trading networks, and design of information disclosure policies. His research has applications to operations of online social networks, ride-sharing platforms, delivery platforms, two-sided marketplaces, supply chains, and online advertising platforms, among others. Ozan Candogan is a recipient of the 2022 Revenue Management and Pricing Section Prize, and a finalist for the 2013 George Nicholson Student Paper Competition and the 2021 M&SOM Service Management SIG Prize. He was also a recipient of the 2009 Siebel Scholarship and the 2012 Microsoft Research Ph.D. Fellowship.

POPNET Connects with Sune Lehmann
2022-12-12 10:00 - 11:00
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Life2vec: Predicting personality, death, emigration, and other life-events from embeddings of registry data

Over the past decade, machine learning has revolutionised computers’ ability to analyze text through flexible computational models. Beyond text, emerging transformer-based architectures have shown promise as tools to make sense of a range of multi-variate sequences from protein-structures to weather-forecasts due to their structural similarity to written language. Another type of process which has a strong structural similarity to language is human lives. From one perspective, lives are simply sequences of events: We are born, we visit the pediatrician, we start school, we move to a new location, we get married, and so on. Here, we use this similarity to adapt innovations from natural language processing to examine the evolution and predictability of human lives based on day-to-day event sequences.

About Sune Lehmann

Sune is a Professor of Networks and Complexity Science at DTU Compute, Technical University of Denmark. He’s also a Professor of Social Data Science at the Center for Social Data Science (SODAS), University of Copenhagen. His work focuses on quantitative understanding of social systems based on massive data sets. A physicist by training, Sune’s research draws on approaches from the physics of complex systems, machine learning, and statistical analysis. He works on large-scale behavioral data and while his primary focus is on modeling complex networks, his research has made substantial contributions on topics such as human mobility, sleep, academic performance, complex contagion, epidemic spreading, and behavior on Twitter.

POPNET Connects with Ágnes Backhausz
2022-11-21 12:00 - 13:00
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The impact of spatial and social structure on an SIR epidemic on a weighted multilayer network

The household model defined and analyzed by Frank Ball consists of households (small cliques of the same size), connected to each other with edges of smaller weight. However, this model does not include dense clusters other than the households themselves, hence, for example, school classes are not represented. Starting from this, in the current work we were interested in the behaviour of an SIR process on a more complex random graph model, based on the layer of households, a layer of schools and workplaces, the layer representing the spatial structure and a fourth layer representing communal places. We studied the sensitivity of the model for the different parameters, looked for estimates of the parameters in a simpler case, and compared different vaccination strategies. Our model and the main results will be presented in the talk.

Joint work with István Z. Kiss, Péter L. Simon and György Székely.

About Ágnes Backhausz

Ágnes Backhausz obtained her PhD in 2013 at ELTE Eötvös Loránd University, Budapest, Hungary, with a thesis on preferential attachment random graphs. As a postdoctoral researcher, she spent two years at Alfred Renyi Institute of Mathematics, working in the “Limits of Structures” research group. Her research topic includes random graphs, graph limit theory, and more recently epidemic spread on random networks. Currently she is an assistant professor at ELTE Eötvös Loránd University, Budapest, Hungary, and also a research fellow at Alfréd Rényi Institute of Mathematics.

Network-based study of segregation and social capital in the Netherlands using population-scale social network data derived from official population registers
2022-11-16 07:00 - 16:00
Conference talk by Yuliia Kazmina at the Dutch Demography Day 2022.

Using population-scale social network data derived from official population registers, we propose a network-based study of segregation and social capital in the Netherlands.

Segregation 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.

Social 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.

Capturing the social fabric: Population-scale socio-economic segregation patterns
2022-11-03 16:15 - 17:15
Conference talk by Yuliia Kazmina at the Odissei Conference for Social Science in the Netherlands 2022 in session 4.1.

Segregation 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.

The anatomy of a population-scale social network
2022-11-03 16:00 - 17:00
Lecture by Eszter Bokányi for the Network Seminar series of the Learning Planet Institute

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.

POPNET Connects with Vincent Traag
2022-10-27 11:00 - 12:00
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Location: Leiden UniverisityLeiden Institute of Advanced Computer Science (LIACS), Room 403Niels Bohrweg 12333 CA Leiden

Large network community detection by fast label propagation

Many 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.

About Vincent Traag

Vincent 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.

Traag 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.

Excess closure in a multilayer population-scale social network
2022-10-21 11:45 - 12:00
Lecture by Eszter Bokànyi at the Conference on Complex Systems

Recent 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].

However, 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].

We 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.

In 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.

Here, 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.

Closure 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.

To 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.

Figure 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.

Our 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.

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.

References [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

POPNET Connects with Floris Vermeulen
2022-09-19 10:00 - 11:00
Please register for the seminar via the button. You will receive a confirmation via email.

What do big data analytics reveal about neighbourhood organisational vitality?

Big 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.

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About Floris Vermeulen

Dr. 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.

The Small-World structure of a population-scale social network
2022-09-13 15:16 - 15:36
Lecture by Frank Takes, Eszter Bokànyi, and Eelke Heemskerk at the 6th European Conference on Social Networks (EUSN 2022).

The 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.

In 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.

Apart 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.

On 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.

Measuring social capital in a population-scale social network
2022-09-13 13:39 - 13:59
Lecture by Bart de Zoete, Frank Takes, Eelke Heemskerk, Eszter Bokànyi and Yuliia Kazmina at 6th European Conference on Social Networks (EUSN 2022).

In 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.

Capturing the social fabric: Population-scale socio-economic segregation patterns
2022-09-13 13:16 - 13:36
Lecture by Yuliia Kazmina, Eszter Bokànyi, Eelke Heemskerk and Frank Takes at the 6th European Conference on Social Networks (EUSN 2022).

Segregation 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.

Anonymity of multi-hop neighborhoods in social networks
2022-09-13 10:46 - 11:06
Lecture by Rachel de Jong, Frank Takes and Mark van der Loo at the 6th European Conference on Social Networks (EUSN 2022).

Introduction & 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.

Previous 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.

Approach. 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.

To 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.

The anatomy of a population-scale social network
2022-09-13 10:23 - 10:43
Lecture by Eszter Bokànyi, Eelke Heemskerk, Yuliia Kazmina and Frank Takes at the 6th European Conference on Social Networks (EUSN 2022).

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.

The anatomy of a population-scale social network
2022-07-21 19:00 - 20:00
Conference poster presentation by Eszter Bokányi at IC2S2 at the Harper Center of the Booth School of Business at the University of Chicago.

Authors: Eszter Bokanyi, Yuliia Kazmina, Rachel de Jong, Frank Takes and Eelke Heemskerk

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 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.

We 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.

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 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.

Figure 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.

Figure 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.

Concluding, 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.

References

1. 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).

2. 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).

3. Bailey, M., Cao, R., Kuchler, T., Stroebel, J. & Wong, A. Social Connectedness: Measurement, Determinants, and Effects. J. Econ. Perspect. 32, 259–280 (2018).

4. Lazer, D. et al. Meaningful measures of human society in the twenty-first century. Nature 595, 189–196 (2021).

5. Onnela, J.-P. et al. Structure and tie strengths in mobile communication networks. Proc. Natl. Acad. Sci. 104, 7332–7336 (2007).

6. Tóth, G. et al. Inequality is rising where social network segregation interacts with urban topology. Nat. Commun. 12, 1143 (2021).

Anonymity of Multi-hop Neighborhoods in Social Networks
2022-07-20 19:20 - 20:20
Conference poster presentation by Rachel de Jong at Harper Center of the Booth School of Business at the University of Chicago.

Authors: Rachel de Jong, Mark van der Loo and Frank Takes

Introduction & 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.

Previous 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.

Approach. 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.

To 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.

Results. 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.

In 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.

Conclusions. 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.

References

Lazer, 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.

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.

Figure 2. Fraction of unique nodes in real-world networks [5, 6, 7], for different values of distance d.
