POPNET

POPNET Connects with Rense Corten
2022-04-19 09:00 - 10:00 (Europe/Amsterdam)
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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.

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 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 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 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”).

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.

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

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.

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.

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

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.

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.

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 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’.

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.

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.

Measuring Structural Anonymity in Complex Networks
2021-08-31 14:00 - 15:00
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.

Measuring Anonymity in Complex Networks
2021-07-31 17:00 - 18:30
Poster presentation by Rachel de Jong at IC2S2 conference

Authors: Rachel de Jong; Mark van der Loo; Frank Takes

Complex networks are often used to describe the interactions between individuals or organizations within social or economic systems. In order to comply with regulations regarding privacy and data protection, such data is frequently anonymized by leaving out personal identifiers of the nodes. However, in such cases (properties of) a seemingly anonymized individual may still be re-identified based on its structural position in the network [1].

This is particularly relevant for National Statistical Institutes (NSIs) that are applying network science to population scale social network data [2]. When releasing data for research purposes, NSIs rely on statistical disclosure control (SDC) techniques for data protection [3]. A central concept in this field is anonymity: the number of equivalent data points in an anonymized data set. Anonymity is an essential component in assessing the risk of disclosure.

This work presents a method for measuring the disclosure probability of nodes in networks, that takes as a parameter the amount of information an adversary has about a node’s surrounding structure. We also provide an in-depth analysis of node anonymity in three well-known complex network models. The proposed j-anonymity distributions can be used to measure disclosure probabilities, which in turn can be used to assess disclosure risk. Numerical experiments reveal that many nodes are de-anonymized when their neighbourhood of radius 2 is known.

The emergence of hierarchy in spatial diffusion over the life-cycle of innovations
2021-07-31 09:00 - 09:15
Conference talk at IC2S2

Authors: Eszter Bokanyi, Martin Novák, Ákos Jakobi and Balazs Lengyel

ABSTRACTUsing a model capturing distance-decay, urban scaling, and hierarchical difference, we show that hierarchical diffusion has an increasing role over the life cycle in the spatial adoption of an online social network.

Population-scale Social Network Analysis
2021-07-30 15:10 - 15:50
Keynote Frank Takes at IC2S2 2021

Missed the keynote by Frank Takes at IC2S2 2021? You can view the full presentation here.

Abstract

The use of country-wide administrative register data enables the discovery of population-scale insights into contemporary problems such as segregation, inequality and poverty. This talk considers responsibly anonymized population-scale social network data on all 17 million inhabitants of the Netherlands. I will 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. Moreover, 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. Finally, I outline the possibilities of population-scale network data for various areas of social science research.

About Frank Takes

Frank Takes is head of the Computational Network Science Lab at Leiden University and research fellow in computational social science at the University of Amsterdam. He is co-director of the Platform for Population-scale Social Network Analysis (POPNET) and board member of the Dutch Network Science Society (NL NetSci). His research deals with methods for large-scale social network analysis, with a focus on applications in economic networks, scientific collaboration networks and population-scale social networks.

Income of home neighbourhood and the structure and concentration of online social ties in US metro areas
2021-07-28 10:45 - 11:00
Conference talk at IC2S2

Authors: Sándor Juhász, Ádám Kovács, Balázs Lengyel and Eszter Bokányi

ABSTRACTThis study shows that online social connections of people in poor neighbourhoods are more spatially concentrated and structurally cohesive than the network of people living in better-off areas of the top 50 metropolitan areas of the US.

The effect of commuting on the income assortativity of social network ties
2021-07-28 09:45 - 10:00
Conference talk at IC2S2

Authors: Eszter Bokanyi, Sándor Juhász, Márton Karsai and Balazs Lengyel

ABSTRACTIn this work, we investigate home-work locations and mutual followership ties of Twitter users from the top 50 metropolitan areas of the United States. We find that despite the heterogeneity of spatial structures in cities, above median commuting reduces the income assortativity of social networks by 30% on average.

The community structure of global scientific collaboration - lecture at Networks2021 conference
2021-07-09 16:15 - 16:30
Lecture by Frank Takes on the Networks 2021 conference

Authors: Hanjo Boekhout; Eelke Heemskerk; Frank Takes

In this work we study the structure of global scientific collaboration, in an attempt to better understand the internationalization of research. In particular, we are interested in the existence of closely collaborating scientific communities. Indeed, it is well-known that even though science knows no borders, collaboration ties are not randomly created between individual scholars. Instead, we anticipate geographical, political and cultural factors to play a crucial role in this process. In this paper we set out to empirically investigate to what extent this is the case by providing a large-scale analysis of 23 million publications from Web of Science in the period 2008—2019.

Measuring Anonymity in Complex Networks at Networks 2021 conference
2021-07-06 20:45 - 21:00
Lecture by Rachel de Jong at Networks2021 conference

Authors: Rachel de Jong; Mark van der Loo; Frank Takes

Complex networks are often used to describe the interactions between individuals or organizations within social or economic systems. In order to comply with regulations regarding privacy and data protection, such data is frequently anonymized by leaving out personal identifiers of the nodes. However, in such cases (properties of) a seemingly anonymized individual may still be re-identified based on its structural position in the network [1].

This is particularly relevant for National Statistical Institutes (NSIs) that are applying network science to population scale social network data [2]. When releasing data for research purposes, NSIs rely on statistical disclosure control (SDC) techniques for data protection [3]. A central concept in this field is anonymity: the number of equivalent data points in an anonymized data set. Anonymity is an essential component in assessing the risk of disclosure.

This work presents a method for measuring the disclosure probability of nodes in networks, that takes as a parameter the amount of information an adversary has about a node’s surrounding structure. We also provide an in-depth analysis of node anonymity in three well-known complex network models. The proposed j-anonymity distributions can be used to measure disclosure probabilities, which in turn can be used to assess disclosure risk. Numerical experiments reveal that many nodes are de-anonymized when their neighbourhood of radius 2 is known.

The Anatomy of Metropolitan Spatial Social Networks at Netstructure 21 satellite
2021-06-23 17:35 - 17:55
Lecture by Eszter Bokányi on Netstructure21 satellite

Spatially embedded social networks have long been at the focus of researchers, but the resolution of most studies was not enough to analyze how connections are formed within large urban areas, and how all this is linked to the socio-economic status of people within the network. First, I am going to show how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. Home and work locations are identified from geolocated tweets that enable us to infer the socio-economic status of individuals. The results suggest that commuting-enabled mixing manifests in reduced levels of income assortativity in online social relationships suggesting a universal role of commuting in integrating disparate social networks in cities. Second, I am going to talk about the difference between the spatial concentration of social ties around people’s home and work locations, and how the concentration around the home locations is stronger for people with lower socio-economic status, whereas higher status users have a higher social connection density around their workplaces. These differences between the poor and the rich can also be observed in certain structural metrics of the ego-networks linked to social capital such as the clustering coefficient and the share of supported ties. This suggests that the structure of social networks around homes provide greater social capital for poor users than for rich users.

POPNET Connects with Zoltán Elekes
2021-06-15 13:00 - 14:30
Using Swedish register data for regional and network science

Drawing on statistical registers, Sweden offers world-class microdata resources for quantitative research. The register data covers the entire population of Sweden in a panel structure, also matching workers to workplaces. As such it provides detailed information on individuals, workplaces, firms and regions over time. In this talk I will give an overview of the use of this dataset, and provide examples of ongoing research based on it in the context of regional and network science. This part will first cover inferring coworker networks to explain individual, firm and regional outcomes. Second, I will show how constructing skill-relatedness networks enables the study of individual wage-mobility, the economic resilience of local labour markets, and helps informing regional policy on economic diversification.

About Zoltan Elekes

Zoltán Elekes is engaged in research on topics at the intersection of evolutionary economic geography and network science. He holds a PhD in economics from the Doctoral School of Economics, University of Szeged, Hungary since 2018. In his thesis he explored how multinational enterprises and import influence the economic diversification of microregions. He is a research fellow at the ANET Lab of the Centre for Economic and Regional Studies in Hungary, where he studies the geography of collaborative knowledge production and supplier-buyer networks. He leads a research project at the Centre for Regional Science at Umeå University, Sweden on how the network structure of local labour markets condition regional economic resilience. He has published in leading field journals including Regional Studies and European Planning Studies, and has refereed for Economic Geography and Regional Studies among others. Zoltán is the 2019 recipient of the Excellent Young Regional Scientist Award of the Hungarian Regional Science Association, the Hungarian section of ERSA. Since 2019 he has been engaged on multiple occasions in consulting policymakers in Swedish regions on smart specialization and regional economic development. He continues to mentor students in Hungary, and contributes to the Network and Spatial Analysis course developed by the ANET Lab.

About POPNET Connects

POPNET Connects is the seminar series of the Population Scale Network Analysis (POPNET) platform. We invite everyone to join the seminar, connect with others and get involved in discussion. Interested in joining? Send us an email or sign up for our mailing list.

POPNET Connects with Yuliia Kazmina
2021-05-18 13:00 - 14:30
Uncovering corruption risks in public procurement using big data: the case of Ukraine

Corruption, favoritism and lack of public accountability have long been a central topic in academic research and policy debates. This study focuses on the phenomenon of corruption in a bureaucratic context, in one of the most corruption-prone sectors – public procurement. In their attempts to fight institutionalized grand corruption, governments all over the world improve the regulatory frameworks to ensure transparent and efficient public procurement market. One of the recent developments in the field was a successful launch of the ProZorro public procurement platform in Ukraine that achieved recognition all over the world as one of the best procurement reforms. Despite the enormous effort invested in the development of the transparent and efficient public procurement platform, it is too early to conclude that ProZorro successfully eliminated corruption. Hence, this study sets out to precisely estimate the prevalence and distribution of corruption risks in ProZorro. Overall, we find that ProZorro faces issues related to the lack of competition such as a low number of suppliers and a high share of single bidding contracts in some of the procurement markets. To tackle these issues, we develop a risk assessment tool for policymakers that could signal higher corruption risks in tendering processes.

About Yuliia Kazmina

Yuliia Kazmina is a PhD Candidate at the POPNET project. She obtained a master’s degree in Economic Policy in Global Markets from the Central European University, Hungary.Yuliia’s research interests concern the domain of computational social science and data-driven policymaking with a focus on network science approach. Previously Yuliia has been a data scientist at a think tank researching and advocating good governance and her policy projects focused on issues of transparency, corruption, and collusion in public funds as well as risks of organized crime.

POPNET Connects with Eszter Bokányi
2021-04-29 14:00 - 15:00 | Online
Thursday 29th April 2021 from 14:00 – 15:00Seminar
The anatomy of metropolitan spatial social networks
Spatially embedded social networks have long been at the focus of researchers, but the resolution of most studies was not enough to analyze how connections are formed within large urban areas, and how all this is linked to the socio-economic status of people within the network. First, I am going to show how commuting distance conditions the online social ties of Twitter users in the 50 largest metropolitan areas of the United States. Home and work locations are identified from geolocated tweets that enable us to infer the socio-economic status of individuals. The results suggest that commuting-enabled mixing manifests in reduced levels of income assortativity in online social relationships suggesting a universal role of commuting in integrating disparate social networks in cities. Second, I am going to talk about the difference between the spatial concentration of social ties around people’s home and work locations, and how the concentration around the home locations is stronger for people with lower socio-economic status, whereas higher status users have a higher social connection density around their workplaces. These differences between the poor and the rich can also be observed in certain structural metrics of the ego-networks linked to social capital such as the clustering coefficient and the share of supported ties. This suggests that the structure of social networks around homes provide greater social capital for poor users than for rich users.
About Eszter Bokányi
Eszter Bokányi is a research postdoc in the POPNET project investigating a population-scale longitudinal social network of the Netherlands. She has a background in physics, and graduated with a PhD from statistical physics at Eötvös Loránd University in Budapest. The topic of her thesis was already rather connected to computational social science, having investigated a huge database of geolocated Twitter messages.Nowadays, she is still mostly interested in spatial social networks and their connection to human mobility, as well as dynamical phenomena such as the spreading of innovations over these social networks.

About POPNET Connects

POPNET Connects is the seminar series of the Population Scale Network Analysis (POPNET) platform. We invite everyone to join the seminar, connect with others and get involved in discussion.
