POPNET

Measuring Structural Anonymity in Complex Networks
2021-08-31 14:00 - 15:00 (Europe/Amsterdam)
Master thesis presentation by Rachel de Jong

When sharing sensitive data, it should be made sure that entities represented in it are sufficiently anonymous in order to avoid a possible breach of privacy. In the field of statistical disclosure control, this concept is well studied. However, thus far the majority of work in this field focuses on microdata and (aggregated) tabular data. In this work, we discuss a new measure for anonymity in networks: d-k-anonymity. It improves upon existing measures (which are in most cases too weak, too strict, or not able to account for triangles) by being parametrized in strictness and taking into account all information in the d-neighbourhood of a vertex. This enables the user to select the right level of anonymity based on how much a possible attacker knows. We present an algorithm that can efficiently measure the anonymity and apply it to three well known-graph models with up to 10,000 vertices, as well as a real-world network; the full family network of the Netherlands, consisting of over 15 million vertices. In our experiments, we find that for graph models most anonymity is lost when measuring 2-k-anonymity, and vertices quickly all become unique as the edge density increases. For the family network, over 2.7 million vertices have an anonymity of 1 when measuring 5-k-anonymity, implying that they are uniquely identifiable when their exact position in their 5-neighbourhood is known.

Supervisors: Frank Takes and Mark van der Loo (CBS)

If you wish to join this presentation, please send an email to popnet@uva.nl.

Population-scale social network analysis
2021-09-09 09:00 - 09:20
Parallel session talk by Frank Takes at European Conference on Social Network EUSN 2021

This work is centered around a population-scale social network analysis study of all 17 million inhabitants of the Netherlands. In the considered (anoymized) population-scale social network, node and edge information stems from register data: official government registers containing highly curated records on family, work, school, household and neighborhood relations. First, we discuss how the considered data is fundamentally different from the type of data commonly used to define connectivity in socials networks, such as survey data, spatiotemporal proximity data or online social media data. To understand how to derive meaningful insights from the considered more formal” social ties, we first revisit some of the fundamental issues in network analysis, relating to the unit of analysis (Butts 2009), measurement errors (Kossinets 2016, Wang et al. 2012) and the boundary specification problem (Laumann 1989, Nowell et al. 2018). Second, we present characteristics of the constructed multilayer social network, in which 17.2 million nodes are connected through 41.1 million household links, 233.8 million school links, 270.2 million family links, 352.7 million neighbor links, 566.0 million work links. In total, there are 1.423 billion unique links between individuals, as some of the layers overlap. As expected, the network as a whole has an overall skewed degree distribution and is highly clustered, the latter in part due to the fact that some layers are in fact projections of underlying two-mode affiliation networks. Third, a more in-depth analysis of the family layer of this multilayer network dataset reveals the family structure of all 17.2 individuals living in the Netherlands. We present unique statistics on the statistical properties of this population-scale family network, consisting of directed parent-child relationships. We do so in light of two concrete examples with relevance in the family studies and sociology literature. Purely based on the structure of this network, we can now for the first time, at scale, validate existing findings and hypotheses in this area. In particular, we look at household composition for children with parents that are no longer together and remarriage behavior of parents with and without children. The two issues above can quantitatively be addressed by investigating at the overlap of the family layer with for example the household layer. Finally, we demonstrate the advantages and disadvantages of using register data as compared to the use of household survey data in the study of family networks, and how the interplay between the family layer and other network layers can be used to answer a plethora of other network-driven socio-economic questions of interest.

POPNET Connects with Lasse Folke Henriksen
2021-09-13 10:00 - 11:00
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Careers through Networks – Studying the Relational Underpinnings of Social Mobility using Danish Register Data

Labor market mobility is the product of people traversing complex interdependent networks of institutions and people. Over the course of careers, workers build social networks to other workers through shared institutional and organizational histories. For a long time, scholars have studied how such networks enable and constraint the mobility of workers, shaping horizontal movement across workplaces as well as vertical movement in the occupational structure. In this talk we theorize the relative importance of different configurations of worker ego networks in enabling social mobility in labor markets, along with their changing utility across different occupational careers.

We consider family, education-, workplace-, and resident-based ties to co-workers and managers and provide a theoretical framework that link synergies between the type, strength and power asymmetry of social ties to horizontal and vertical mobility outcomes. We present various analytical strategies for identifying workers’ network configurations and for testing the relative importance of different configurations on different mobility outcomes. Combining linked employer-employee data with population registers and educational records we reconstruct workers’ network configurations as they move between workplaces. By combining dynamic multimodal network data with mobility outcomes across workers’ careers we are able to demonstrate the multiple pathways by which networks inform labor market mobility.

About Lasse Folke Henriksen

Lasse Folke Henriksen is Associate Professor at Copenhagen Business School. Henriksen’s research interests involve: social networks in organisations and markets; experts and professions in governance and policy; the socio-economic and political prominence of corporate elite; inequality in a comparative perspective; and the politics of conservation and environmental sustainability. His work frequently deploys social network analytic tools to trace the origins of social and political action. Henriksen is the author of several books and he has published in journals such as Organization; Social Networks; Regulation & Governance; Global Networks; and International Political Sociology.

POPNET Connects with Tobias Blanke
2021-09-27 10:00 - 11:00
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Algorithmic Reason – The New Government of Self and Other

Tobias Blanke will present parts of their forthcoming book (together with Claudia Aradau) on “Algorithmic Reason – The New Government of Self and Other”. He will focus on the big data debates as they are pertinent to fundamental questions of the relations between the governing of individuals and populations before focussing on a case of how these translate into the identification of ‘others’ in the global war on terror using network analysis.

About Tobias Blanke

Tobias Blanke is Distinguished University Professor of Artificial Intelligence and Humanities at the University of Amsterdam and project partner of POPNET.

His academic background is in moral philosophy and computer science. Tobias’ principal research interests lie in the development and research of artificial intelligence and big data devices as well as infrastructures for research, particularly in the human sciences. Recently, he has also extensively published on ethical questions of AI like predictive policing or algorithmic otherings, as well as critical digital practices and the engagement with digital platforms.

Tobias’ monographs include most recently Digital Asset Ecosystems – Rethinking Crowds and Clouds, which offers a new perspective on the collaboration between humans and computers in global digital workflows. He is currently writing a book on the socio-economic position of AI called ‘Algorithmic Reason – the Governance of Self and Other’.

3rd NETWORKS Match Makers Seminar
2021-10-01 12:00 - 17:00
Lecture by Frank Takes

The Networks Match Makers seminar series started in October 2020 after a succesfull workshop in January 2020, called NETWORKS Matchmaking Event. In this series we bring together network scientists from the social and economic sciences with network scientists from mathematics and computer science, with the goal to explore the opportunity to build synergies.

The third seminar takes place in the afternoon of October 1, 2021, and will be held online.

Frank Takes is assistant professor at the computer science department (LIACS) of Leiden University. Frank will talk about ‘Population-scale social network analysis’.

Population-scale social network analysis

This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. The data is sourced from country-wide administrative register data, enabling the discovery of population-scale insights into a society. I will show how the analysis of a population-scale multilayer network of family, work, school, household and neighborhood relations enables us to revisit the well-known small-world phenomenon from a unique angle. Moreover, I discuss how the type of formal links in this social network require one to critically rethink network analysis concepts such as the unit of analysis, measurement errors effects and the boundary specification problem.

POPNET Connects with Milena Tsvetkova
2021-10-11 10:00 - 11:00
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Using networks to study inequality: two examples

Do daily decisions and social interactions reproduce socioeconomic inequality? Limited resources could drive self-defeating behaviour, strain interactions with others, and restrict access to valuable information in ways that reinforce people’s disadvantaged position, while already abundant resources could beget additional advantages in ways that make the rich richer. I will present two projects that use radically different computational and analytical methods to address this general hypothesis. In the first example, we conduct online network cooperation experiments to study whether the visibility of outcome-relevant resources (ability, intelligence, knowledge, etc.) and the visibility of wealth could improve inequality. In the second example, we analyse the bipartite network of verified Twitter accounts of companies, brands, and organizations and their followers to estimate the socioeconomic status of individual Twitter users.

About Milena Tsvetkova

Milena Tsvetkova is Assistant Professor of Computational Social Science at the Department of Methodology at the London School of Economics and Political Science. She completed her PhD in Sociology at Cornell University and postdoctoral research at the Oxford Internet Institute. Her research interests lie in the fields of computational social science. She uses large-scale web-based social interaction experiments, network analysis of online data, and agent-based modelling to investigate fundamental social phenomena such as cooperation, social contagion, segregation, and inequality.

POPNET Connects with Márton Karsai
2021-11-16 10:00 - 11:00
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Socioeconomic correlations in social-communication networks and mobility

Our understanding of the structure and dynamics of social systems has been developed considerably during the last years due to the recent availability of large digital datasets collecting interactions of millions of individuals. However, although these studies consider the structural, temporal, or spatial characters of human interactions they commonly miss one important dimension regarding the socioeconomic status of individuals, which may largely determine the social structure itself. The uneven distribution of wealth and individual economic capacities are among the main forces, which shape modern societies and arguably bias the emerging social network. In this talk, we will discuss a set of results aiming to close this gap through studies relying on various data-driven observations on mobile-phone communication, bank transaction, satellite, online social system and human mobility datasets. We will find that socioeconomic disparities lead to segregation patterns not only in space but in the social network structure and mobility patterns of people.

About Márton Karsai

Márton Karsai, PhD, Habil., is an Associate Professor in the Department of Network and Data Science at the Central European University, researcher at the Rényi Institute of Mathematics, and fellow of the ISI Foundation in Torino. His research interest falls within human dynamics, computational social science, and data science, especially focusing on heterogeneous temporal dynamics, spatial and temporal networks, socioeconomic systems and social contagion phenomena. His main expertise is in analysing large human interaction datasets and in the development of data-driven models of various social phenomena.

ODISSEI Community Conference 2021
2021-11-18 11:00 - 16:30
Missed the lecture by Frank Takes at the ODISSEI Community Conference 2021? You can view the full talk here at 1:01:29.

Co-director of POPNET Frank Takes will be speaking at the ODISSEI Community Conference during the session “Innovating Computational Social Science research projects”. The session showcases current computational research projects that are conducted within the social sciences.

About the ODISSEI Community Conference:

The Community Conference 2021 will be an opportunity to meet those who are building and using ODISSEI in person, through a series of highly interactive and stimulating sessions designed to encourage collaborations and inspire new lines of research. Sessions will encompass everything from the latest developments in survey science through to projects utilizing the ODISSEI Secure Supercomputer. Frank Pijpers, professor by special appointment of Complexity for Official Statistics (CBS and UvA), will give a keynote lecture. The conference will take place in the Muntgebouw in Utrecht on Thursday, 18 November from 12:00 to 17:30 hours. Attendance is free of charge.

Programme:

12:00Lunch with ODISSEI Facilities Fair13:00Welcome by Pearl Dykstra (ODISSEI)13:05Keynote: ‘Network Reconstruction: Why and How?’– Frank P. Pijpers (Statistics Netherlands (CBS) and University of Amsterdam)13:45Innovating Computational Social Science research projectsThis session showcases current computational research projects that are conducted within the social sciences. They use pseudonymised CBS microdata for their analysis with a variety of innovating approaches: on the ODISSEI Secure Supercomputer, in combination with LISS panel data, or within the new research infrastructure POPNET that is supported by the Platform Digital Infrastructure Social Sciences & Humanities (PDI-SSH). ‘Filling in the blind spots: income and wealth of households’– Marike Knoef (Leiden University)‘Population-scale social network analysis‘– Frank Takes (Leiden University, POPNET)‘Geographic visualization of childhood opportunities in the Netherlands using the OSSC Secure Supercomputer’– Bastian Ravesteijn (Erasmus University Rotterdam and KansenKaart.nl)14.30Open Science with Secure Data One of the biggest challenges within computational social science is how to share research that is conducted with sensitive data in a secure environment to protect privacy. This session focuses on how researchers are currently dealing with this challenge, and discusses highly promising new ways to answer present-day ambitions to make computational social science more open and FAIR.‘Collaborating when using sensitive data in a secure environment‘– Bas van der Klaauw (VU Amsterdam)‘The big workaround: an open processing and analysis pipeline for closed data‘– Erik-Jan van Kesteren (Utrecht University and ODISSEI SoDa Team) Respondent: Melanie Imming – independent consultant specialized in Open Science and FAIR data15.15Coffee break15.30Linking large datasets in social science and humanitiesBoth the social sciences and the humanities are making massive strides in digitizing and linking large datasets. How do these fields overlap, and what are shared challenges? This session will explore current projects and the ways in which social scientists and scholars in the humanities can benefit from each other’s experiences and strengthen each other’s work.Confirmed speaker:‘Creating life course datasets from historical population sources: the case of Suriname 1830-1950‘– Rick MouritsFurther speakers are to be announced.16.15Closing remarks by Pearl Dykstra16.30DrinksPlease note that at the Muntgebouw, all COVID-regulations, including those for restaurants, apply. Those attending the conference are therefore kindly requested to bring their CoronaCheck QR code.

Location

This year the ODISSEI Community Conference will take place at:MuntgebouwLeidseweg 903531 BG UtrechtView on Google maps

ODISSEI Lunch Lecture: Population Scale Social Network Analysis
2021-12-09 11:00 - 12:00
Lecture by Eelke Heemskerk

POPNET is a novel digital infrastructure and research community with the aim of unlocking longitudinal social network data on the entire population of the Netherlands for academic research purposes. It enables new exciting research in an anonymized as well as ethically and legally responsible manner. This research may lead to actionable insights into key issues including segregation, substantive social change, and UN sustainable development goals such as reducing inequality.Research infrastructureA first-of-a-kind research infrastructure tailored in terms of hard- and software specifically for large-scale social network analysis will be developed. Rich methods from social network analysis and network science will be implemented to unveil new and previously unknown knowledge about the complexity of the Dutch population. This requires the development of an infrastructure that facilitates this kind of research in terms of computation power, memory and data management.Social network analysis researchPilot social network analysis studies will be performed on the (anonymized) population-scale social network of the Netherlands, consisting of 17 million nodes (people) and their hundreds of millions of family, work, school and neighbourhood links as well as demographic attributes. The research, which has a strong computational social science character, will be done in parallel with the building of the research infrastructure to ensure alignment of the developed platform and researcher needs.

About the ODISSEI Lunch Lecture Series

The ODISSEI Lunch Lectures highlight methodological issues and innovations in Social Science.

POPNET Connects with Lőrincz László
2021-12-14 10:00 - 11:00
The role of skills in local and global coworker networks

Social connections that reach distant places are advantageous for individuals, firms and cities, providing access to new skills and knowledge. However, systematic evidence on how firms build global knowledge access is still lacking. In this paper, we analyse how global work connections relate to differences in the skill composition of employees within companies and local industry clusters. We gather survey data from 10% of workers in a local industry in Sweden, and complement this with digital trace data to map co-worker networks and skill composition. This unique combination of data and features allows us to quantify global connections of employees and measure the degree of skill similarity and skill relatedness to co-workers. We find that workers with extensive local networks typically have skills related to those of others in the region and to those of their co-workers. Workers with more global ties typically bring in less related skills to the region. These results provide new insights into the composition of skills within knowledge-intensive firms by connecting the geography of network contacts to the diversity of skills accessible through them.

About Lőrincz László

László Lőrincz is a Sociologist (Ph.D) at NETI Lab at Corvinus University, and ANet Lab at Research Centre for Economic and Regional Studies. He joined the Research Centre for Economic and Regional Studies in 2013, after working in the government, and in the private sector. He works at Corvinus University since 2016. His research includes adoption and collapse of online social networks, and network aspects of labor mobility and migration. His work was published in Social Networks, Journal of Technology Transfer and Applied Network Science.

ANET Lab Seminar Series: Frank W. Takes
2021-12-17 09:00 - 10:00
Frank W. Takes (Leiden University & University of Amsterdam): Population-scale Social Network Analysis

Abstract | Country-wide administrative register data, as studied within the POPNET project, enables the discovery of population-scale insights into contemporary social scientific problems such as segregation, inequality, loneliness and poverty. This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. We discuss how the formal links in this social network require one to critically rethink network analysis concepts such as the unit of analysis, measurement errors effects and the boundary specification problem, but also allows us to in a unique way revisit the well-known small-world phenomenon. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population.

Bio | Frank Takes is head of the Computational Network Science Lab at LIACS, the computer science department of Leiden University. He is also co-PI of the Population-scale social network Analysis (POPNET) platform, involving a collaboration between Leiden University, University of Amsterdam and Statistics Netherlands (CBS). His research deals with understanding the connectivity of our highly connected society, and is fascinated by how complex network structures cause societies or economies to fail or succeed. He is a steering committee member of the International Conference on Computational Social Science and a board member of the Dutch Network Science Society, an official chapter of the International Network Science Society. For more information, see https://franktakes.nl

Population-scale Social Network Analysis
2022-01-21 15:00 - 16:00
Seminar at GRAFO – GRUP DE RECERCA EN ANTROPOLOGIA FONAMENTAL I ORIENTADA (University of Barcelona) by Frank Takes and Yuliia Kazmina

Country-wide administrative register data, as studied within the POPNET Project, enables the discovery of population-scale insights into contemporary social scientific problems such as segregation, inequality, loneliness, and poverty. This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. We discuss how the formal links in this social network require one to critically rethink network analysis concepts such as the unit of analysis, measurement errors effects and the boundary specification problem, but also allows us to in a unique way revisit the well-known small-world phenomenon. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population as well as emerging socio-economic inequality and segregation patters.

POPNET Connects with David Schoch
2022-02-08 10:00 - 11:00
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Rethinking one-mode projections

Two-mode networks are usually analyzed in one of two ways. With the “direct” approach using methods tailored for bipartite graphs, or with the “conversion” approach, which includes all methods that project the two-mode network onto each mode separately.In this talk, I focus on one-mode projections where one mode serves as the primary mode and the second mode only as a proxy for relations among actors in the primary mode. Drawing parallels to item response theory, I argue that projected (and dichotomized) ties are conceptually different than traditional ties, which therefore restricts the applicability and interpretability of standard network analytic tools in such cases. I will introduce a set of alternative methods to analyze one-mode projections and exemplify these with several empirical examples.

About David Schoch

David came to Manchester in September 2018 as a Presidential Fellow in Sociology. He received his PhD in 2015 at the Department of Computer and Information Science, University of Konstanz, Germany. During that time, He was also a member of the Graduate School of Decision Sciences. His thesis focused on theoretical advancements for network centrality in the field of social network analysis. He continued as a postdoctoral researcher at the University of Konstanz (11/2015-10/2017) and ETH Zurich (11/2017-8/2018). David also holds a diploma in economathematics from the Karlsruhe Institute of Technology, Germany.In his current research, he focuses on methodological and theoretical contributions to the field of Social Network Analysis. Additionally, he is involved in a project on disinformation campaigns on social media platforms (“political astroturfing”).

POPNET Connects with Fariba Karimi
2022-02-21 10:00 - 11:00
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Network Inequality: Emergence of inequalities and bias in social networks

In this talk, I show how fundamental properties of social interactions such as homophily can result in the emergence of inequalities and biases in society and algorithms and what societal consequences it has on the visibility of minorities.

About Fariba Karimi

Fariba Karimi is leading the Network Inequality group at Complexity Science Hub.

Her expertise encompasses network analysis, computational social science, data science, and agent-based modeling. Her current research focuses on emergence of inequalities and biases in social networks and online algorithms. She has recently awarded a Digital Humanism grant to study the impact of algorithms on exacerbating social inequalities.

Her research appears in leading journals including Nature Human Behavior, Scientific Reports, Nature Humanities and Social Sciences Communications, Advances in Complex Systems, and EPJ Data Science. She is among the 7 candidates for the Hedy Lamarr Prize of the city of Vienna honoring women researchers in Austria for their outstanding achievements in the field of information technology.

POPNET Connects with Marjolijn Das
2022-03-22 09:00 - 10:00
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Using a whole population network in the social sciences

Network research can have enormous added value in different substantive research fields, ranging from epidemiology and infectious disease control to economics and the social sciences. This talk focuses on the use of integral administrative register data within the social sciences, in particular the Dutch whole population network which contains links between neighbours, household members, family, colleagues and classmates. I will present social research done at CBS with register data and with the network, such as contagion of demographic behaviour, segregation, and clustering of crime within networks. I will also sketch avenues for future social research.

About Marjolijn Das

Marjolijn Das works as a senior statistical researcher at Statistics Netherlands and is an endowed professor of Urban Statistics at the Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, appointed within the Leiden-Delft-Erasmus Centre for BOLD Cities. Her research focuses on quantitative social research with large-scale register data. For a number of years, she has been working with the whole population network derived by CBS. Her research theme is the interplay between people and their urban/social environment. She published on spatial inequalities, mobility in the life course, social and family networks and the intergenerational transmission of education. She holds a PhD in Ethology from Utrecht University.

POPNET Connects with Willem Boterman
2022-04-05 09:00 - 10:00
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School choice and school segregation

School segregation is both a result and a cause of educational inequalities in societies world-wide. Understanding mechanisms for the emergence of school segregation is crucial for understanding potential policy solutions. A vast literature has identified a number of main factors in school segregation of which residential patterns and school choice are arguably the most important. Drawing on findings from a range of qualitative and quantitative studies, I will outline the complexity of school segregation and suggest to combine existing approaches with a complexity science perspective. I will present both empirical evidence from the Netherlands and insights from theoretical models.

About Willem Boterman

Willem Boterman is Associate Professor Urban Geography at the University of Amsterdam. He combines qualitative and quantitative methods in his interdisciplinary work into the relationship between spatial and social inequalities. His work is primarily concerned with segregation in neighborhoods and schools, but also with formations of social class and gender.

POPNET Connects with Rense Corten
2022-04-19 09:00 - 10:00
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Social networks research with digital traces data

The emergence of the internet and its various modes of online interaction have created unprecedented opportunities for social scientists to study classic social questions in new ways, but also to ask new questions. This holds in particular for themes like social networks, social order, and cooperation. In this talk I will present various examples of my work over the past years on these themes, drawing on a variety of different “digital traces” data sources, including social media and online markets.

About Rense Corten

Rense Corten is Associate Professor at the Department of Sociology. His research revolves around the themes of cooperation, trust, and (the dynamics of) social networks, with empirical applications including adolescent networks, social media, the sharing economy, online criminal networks, and laboratory experiments. In 2016 he received an NWO Vidi grant for a research project on the origins and consequences of trust in the sharing economy.He obtained his PhD in social sciences in 2009 and his doctorate in sociology in 2004 at Utrecht University.

Research Colloquium on Business Informatics
2022-04-21 15:15 - 16:15
As part of the Research Colloquium on Information Systems and Data Science of the Institute of Information Systems of Leuphana University Lüneburg, co-director Frank Takes will speak on “Population-scale Social Network Analysis” via Zoom.

Abstract

This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. The data stems from country-wide administrative register data, and has the potential to shed new light on contemporary social scientific problems such as segregation, inequality, loneliness and poverty. The talk discusses how the formal links (family, household, work, school and neighbor ties) in this social network require one to critically rethink network analysis concepts such as the unit of analysis, measurement errors effects and the boundary specification problem. Moreover, it allows us to in a unique way revisit the well-known concept of closure and the small-world phenomenon in a population-scale social network context. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population.

LCN2 seminar: The anatomy of a population-scale social network
2022-04-22 15:00 - 16:00
Lecture for Leiden Complex Networks Network (LCN2) by Eszter Bokányi.Title: The anatomy of a population-scale social network

Abstract: 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.

Population-scale Social Network Analysis
2022-04-29 16:00 - 17:00
Lecture by co-director Frank Takes for the Maths and Statistics Department in the University of Limerick.

Abstract

This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. The data stems from country-wide administrative register data, and has the potential to shed new light on contemporary social scientific problems such as segregation, inequality, loneliness and poverty. The talk discusses how the formal links (family, household, work, school and neighbor ties) in this social network require one to critically rethink network science concepts such as the unit of analysis, measurement errors effects and the boundary specification problem. Moreover, it allows us to in a unique way revisit the well-known concept of closure and the small-world phenomenon in a population-scale social network context. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population.

How anonymous are you?!
2022-05-03 15:00 - 19:00
Statistics Netherlands (CBS) has enormous amounts of data at its disposal, from which social networks are also derived. But how anonymous are you in this data? Is it possible for hackers to extract sensitive information from these data sets, by combining them with other data sources?

In the hackathon ‘From Person to Open Data’, you, as a ‘potential attacker’, will search for network relations of a given list of people. If you are able to find the most relations within 4 hours, you receive a prize!

Interested? Sign up for free before April 27th! The link to the registration form is available on Brightspace, or request the registration link via popnet@uva.nl.Pizza and drinks at the end of the day are included, and during the hackathon, we will provide beverages and snacks.

TimeProgramLocation14:45Walk-inRoom 40515:00IntroductionRoom 40515:10AssignmentRoom 302-30419:00End of assignmentPizza and drinksFooBar19:30Prize ceremony FooBar

For whom?Students. You don’t have to speak Dutch, but it can be advantageous, since the data is about Dutch people. You can sign up as a team of two, but it is also possible to sign up individually. If you want, we will pair you up with another student on the day itself.

LocationLeiden University, Snellius buildingNiels Bohrweg 1, 2333 CA Leiden

OrganisationThis hackathon is organized by the Anonymity in Complex Networks project (ANO-NET), the Methodology department of Statistics Netherlands (CBS), the Population-scale Social Network Analysis (POPNET) project of the University of Amsterdam and Leiden University and the Computational Network Science (CNS) research group of Leiden University.

POPNET Connects with Naja Hulvej Rod
2022-05-10 09:00 - 10:00
Please register for the seminar via the button. You will receive a link to the virtual meeting via email.

Social phenomena and health: exploring the role of networks and group dynamics

Reducing health inequalities is a major public health priority, but fundamental questions such as how multifaceted and socially patterned diseases like type 2 diabetes, respiratory diseases and mental disorders behave in populations remain unanswered. To address these questions, health science needs to evolve from primarily focusing on individual exposures and single diseases to a system-oriented approach, considering the dynamics between diseases and events at an individual level, and the subsequent group dynamics at a population level. This talk will present empirical results on social phenomena such as social networks, workplace social capital and childhood adversity in families which arise from group dynamics and relate them to population health.

About Naja Hulvej Rod

Naja Hulvej Rod is Professor of Epidemiology and Chair of the Section of Epidemiology, University of Copenhagen. She is leading the Complexity and Big Data Group, which aim at studying the social and biological factors determining health and disease across the life span. She has extensive expertise in working with longitudinal datasets, register-based research and complex modelling including social influences and group dynamics. To embrace complexity in epidemiology, she actively explores new sources (e.g. smartphones and geocoding) of ‘big data’, incorporate system thinking and leverage insights across disciplines, and she has been involved in several citizen science projects with a direct societal engagement and impact. Naja Hulvej Rod is PI of the Danish Life Course Cohort (DANLIFE) Study, the Well-being in Hospital Employee Cohort (WHALE) study, the SmartSleep program, and the Corona Minds project.

The anatomy of a population-scale social network
2022-05-12 13:30 - 15:00
Lecture for the Institute for Analytical Sociology of Linköping University, by Eszter Bokányi.

Abstract: 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.

POPNET Connects with Tamas David-Barrett
2022-05-17 09:00 - 10:00
Please register for the seminar via the button. You will receive a confirmation via email.

Structural microfoundation theory

You are my love. You are my sister. You are my friend. A trivial fact of our species’ social life is that human social network edge type vary. This variation is not only important for each of these relationships, but also for the structure of the social network around us. This talk will outline the theoretical models for what happens to the social network structure when the bulk of these relationships change. Our societies shift from kinship network to friendship networks due to falling fertility, urbanisation, and migration. Second, the talk will offer an overview the existing empirical evidence using large datasets, and suggest explicit empirical hypotheses. The final part will cover how three further phenomena is predicted by this theory, and ideas of how to test these: the rise of modern law, value fundamentalism, and fake news.

About Tamas David-Barrett

Tamas David-Barrett is an evolutionary behavioural scientist, whose research asks what traits allow humans to live in large and culturally complex societies. He is especially interested in the architecture of social networks, and the evolutionary origins of social network building traits. Tamas’s structural micro-foundation theory offers a new understanding of human societies, and brings biological and social science models under a shared umbrella.

Currently, Tamás is based in Oxford where he teaches at Trinity College. He was educated in London, Cambridge, Jerusalem, and Budapest. Before becoming an academic, he ran a research consultancy and worked all around the planet. He recently finished his book, Matriocracy: The Science of Gender Rules. He is the host of the State of Species annual lecture, and is currently working on a new book: How to Think Scientifically, which tells the natural history of social and scientific truths.

Dutch Network Science Society Symposium 2022
2022-05-19 08:30 - 16:00
The Dutch Network Science Society brings together researchers in network science in the Netherlands from various disciplines, including math, physics, computer science, social sciences, economics and health sciences. There will be a program of invited talks from across the spectrum of research in network science. In addition, there will be plenty of opportunities for the community to discuss, socialize and enjoy food and drinks together.

Program

9:30Walk in9:45Opening9:50Uniform sampling of random networks with sequential construction – Ivan Kryven, Universiteit Utrecht10:25The anatomy of a population-scale social network – Eszter Bokányi, Universiteit van Amsterdam/ Leiden University11:00Coffee break11:20From graphs to hypergraphs randomization – Tiziano Squartini, IMT Lucca & Institute for Advanced Study UvA11:55Using machine learning to enhance graph processing algorithms – Ana Lucia Varbanescu, Universiteit Twente & UvA12:30Lunch13:30Empirically calibrated network simulation as a tool to study social interventions – Christian Steglich, Rijksuniversiteit Groningen14:05Multiscale network neuroscience: embracing the complexity of the human brain network – Linda Douw, Vrije Universiteit14:40Industry & Society showcases15:10Coffee break15:40Using network models to describe, predict, understand, and treat mental disorders – Eiko Fried, Universiteit Leiden16:15Dutch Network Science Society Young Talent Prize Award – Bastian Prasse, European Centre for Disease Prevention and Control17:00Drinks

Population-scale Social Network Analysis
2022-06-16 13:00 - 14:00
Lecture by Frank Takes for the Interaction Data Lab of the Center for Research and Interdisciplinarity (CRI) in Paris.

Abstract

This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. The data stems from country-wide administrative register data, and has the potential to shed new light on contemporary social scientific problems such as segregation, inequality, loneliness and poverty. The talk discusses how the formal links (family, household, work, school and neighbor ties) in this social network require one to critically rethink network science concepts such as the unit of analysis, measurement errors effects and the boundary specification problem. Moreover, it allows us to in a unique way revisit the well-known concept of closure and the small-world phenomenon in a population-scale social network context. The talk furthermore presents initial findings on the relation between the network structure and spatial distribution of the population.

Capturing socio-economic bubbles
2022-07-13 15:20 - 15:30
Lightning talk by Yuliia Kazmina at Women in Network Science & Diversify NetSci joint satellite

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.

Capturing the social fabric: population-scale socio-economic segregation patterns
2022-07-18 23:00 - 2022-07-22 22:59
Conference poster presentation by Yuliia Kazmina at Harper Center of the Booth School of Business at the University of Chicago (online talk)

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

Segregation 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.

The 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”.

To 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.

Estimated 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.

Figure 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.

First, 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.

Third, 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.

Although 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.

Concluding, 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.

The 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.

References

Freeman, 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.

Figure 1. Social opportunity structures of the households in Amsterdam, each subfigure displaying a different context:

a) administrative neighborhood (assortativity: 0.035)

b) close neighbors (assortativity: 0.118)

c) family (assortativity: 0.124)

d) school (assortativity: 0.114)

e) workplace (assortativity: 0.123)

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.

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).
