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DTSTART;TZID=Europe/Amsterdam:20221021T114500
DTEND;TZID=Europe/Amsterdam:20221021T120000
DTSTAMP:20260503T214721
CREATED:20221014T153015Z
LAST-MODIFIED:20221110T100100Z
UID:954-1666352700-1666353600@www.popnet.io
SUMMARY:Excess closure in a multilayer population-scale social network
DESCRIPTION:Lecture by Eszter Bokànyi at the Conference on Complex Systems \n\n\n\nRecent studies on large-scale social networks successfully utilise the growing abundance of digital data sources such as online social networks or mobile communication datasets to uncover fundamental insights on human interaction [1\, 2\, 3].  \n\n\n\nHowever\, in most of these social network data sources\, the sample of people that are represented by the nodes is biased\, and lack of demographic data makes it hard to assess representativity. Moreover\, it is often not clear what exact social relations these online or communication ties represent\, thus\, it is difficult to interpret findings when the goal is to derive meaningful conclusions about people’s social ties [4].  \n\n\n\nWe overcome a number of these drawbacks by presenting a thorough analysis of the complete structure of a 17M node population-scale social network of the Netherlands containing roughly 1.6B edges. This network is derived from highly curated official data sources of the country’s national statistics institute and includes every registered resident in 2018. The edges cover several social relationships: family\, household\, work\, school\, and neighbor. We model each of these edge types as a layer of a node-aligned multilayer network.  \n\n\n\nIn addition\, we have rich individual-level demographic and socio-economic attributes on the nodes (people) available. We consider the network to be a representation of the social opportunity structure in the Netherlands.  \n\n\n\nHere\, we present the first results that show how this population-scale social network is markedly different from many of the large-scale social networks we typically study and reflect on the consequences for computational social science. Below\, we in particular do so by revisiting the well-known concept of closure.  \n\n\n\nClosure is important because individuals have very different resource structures encoded into their social relationships throughout their lives or across demographic groups\, which affects their access to opportunities and information [5]. However\, if we choose to measure closure in a complete population scale social network through traditional local clustering coefficient on the separate layers\, we would get values close to 1. By unioning edges from all layers\, despite the average local clustering coefficient low- ering to 0.40\, it is still unable to resolve potential overlaps or bridges between edges from different layers in people’s egonetworks.  \n\n\n\nTo overcome this problem\, we propose a normalized clustering coefficient that we call excess closure\, that fully exploits the multilayer structure of the networks\, and captures the fraction of triangles in people’s social circles that span across multiple types of relationships.  \n\n\n\nFigure 1 shows how degree and excess closure change with age (a demographic attribute) in the population. Young children have low degrees and very high excess closure since they are only part of family\, neighborhood\, and household structures. Subsequent levels of education paired with working opportunities come with both an increasing median degree\, and decreasing excess closure\, reaching its minimum Fig. 1. Median degree (red) and median excess closure (blue) in ego networks of people of a certain age. Shaded areas are the 25th and 75th percentiles for each age year. around the university age. Working years are characterized by a slight increase in closure\, and gradually decreasing degree\, giving place to low degrees and increased closure in retirement years.  \n\n\n\nOur new normalized multilayer clustering coefficient measure excess closure helps to analyse complete large-scale social networks. The measure captures overlap and bridging between edges of different types in the egonetwork of an individual. We find that excess closure varies across demographic groups as well as throughout people’s lives and it gives a more finegrained understanding of closure in multi-layer population-scale social network data. Our results show a sharp transition from closed to open network structures as young adults engage in higher levels of education\, and a reverse process as people retire. These measurements are first steps in building both methods and universal insights on the rich network structure of highly curated population-level network datasets.  \n\n\n\nFig. 1. Median degree (red) and median excess closure (blue) in ego networks of people of a certain age. Shaded areas are the 25th and 75th percentiles for each age year.\n\n\n\nReferences [1] N. Eagle\, A. S. Pentland\, and D. Lazer. “Inferring Friendship Network Structure by Using Mobile Phone Data.” In: Proceedings of the National Academy of Sciences of the United States of America 106.36 (2009)\, pp. 15274–15278. [2] P. S. Park\, J. E. Blumenstock\, and M. W. Macy. “The Strength of Long-Range Ties in Population-Scale Social Networks”. In: Science 362.6421 (2018)\, pp. 1410–1413. [3] M. Bailey et al. “Social Connectedness: Measurement\, Determinants\, and Effects”. In: Journal of Economic Perspectives 32.3 (2018)\, pp. 259–280. [4] D. Lazer et al. “Meaningful Measures of Human Society in the Twenty-First Century”. In: Nature 595.7866 (2021)\, pp. 189–196. [5] G. T  ́oth et al. “Inequality Is Rising Where Social Network Segregation Interacts with Urban Topology”. In: Nature Communications 12.1 (2021)\, p. 1143
URL:https://www.popnet.io/events/excess-closure-in-a-multilayer-population-scale-social-network/
CATEGORIES:Conference talk
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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20221027T110000
DTEND;TZID=Europe/Amsterdam:20221027T120000
DTSTAMP:20260503T214721
CREATED:20221013T092705Z
LAST-MODIFIED:20221026T141059Z
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SUMMARY:POPNET Connects with Vincent Traag
DESCRIPTION:Please register for the seminar via the button. You will receive a confirmation via email.  \n\n\n\n\nRegister to attend online\n\n\n\n\n \n\n\n\n\nRegister to attend on-site\n\n\n\n\nLocation: Leiden UniverisityLeiden Institute of Advanced Computer Science (LIACS)\, Room 403Niels Bohrweg 12333 CA Leiden \n\n\n\nLarge network community detection by fast label propagation\n\n\n\nMany networks exhibit some community structure. There exists a wide variety of approaches to detect communities in networks\, each offering different interpretations and associated algorithms. For large networks\, there is the additional requirement of speed. In this context\, the so-called label propagation algorithm (LPA) was proposed\, which runs in near linear time. In partitions uncovered by LPA\, each node is ensured to have most links to its assigned community. We here propose a fast variant of LPA (FLPA) that is based on processing a queue of nodes whose neighbourhood recently changed. We test FLPA exhaustively on benchmark networks and empirical networks\, finding that it runs up to 700 times faster than LPA. In partitions found by FLPA\, we prove that each node is again guaranteed to have most links to its assigned community. Our results show that FLPA is generally preferable to LPA. \n\n\n\nAbout Vincent Traag\n\n\n\n\n\n\n\nVincent Traag is a senior researcher at the Centre for Science and Technology Studies (CWTS) of Leiden University in the Netherlands. He leads the research line on modelling the research system within the Quantitative Science Studies (QSS) research group. His main interests are mathematical models in the social sciences with a focus on (social) networks. In addition to his scientific research\, Traag also acts as a bibliometric consultant at the CWTS. \n\n\n\nTraag obtained his Master in sociology (cum laude) from the University of Amsterdam (2008). Coming from a computer science background\, and taking up mathematics during his studies in sociology\, he went on to obtain a PhD in applied mathematics in Louvain-la-Neuve\, Belgium (2013). During his PhD he studied methods for detecting communities in complex networks\, resulting in a Python software package. In addition\, he applied this methodology in several fields across the (social) sciences\, ranging from citation networks to international relations. He joined the CWTS in 2015.
URL:https://www.popnet.io/events/popnet-connects-with-vincent-traag/
CATEGORIES:Seminar
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