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LCN2 seminar: The anatomy of a population-scale social network
April 22 , 16:00 – 17:00 UTC+2
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.