The development of information communication technology has changed the society, and, at the same time, it has a major impact on the scientific approach to study social systems. Big Data provides information at the societal scale and reflect almost all aspects of social life as digital footprints with very good statistics. The analysis of such data and other observations has already led to a number of “stylized facts” about the system of social interactions, including the characterization of the degree distribution, the correlation between tie strength and network topology, assortative mixing by degree, high clustering, overlapping community structure, multiplexity, as well as mechanisms of tie formation and fading. We have constructed a series of multi-agent models, which increasingly reflect the observations. Our aim has been at this stage to shed light on the mechanisms rather than to achieve quantitative agreement. The Weighted Social Network (WSN) model produces Granovetterian correlations between tie strength and topology, and we have explored the roles of the different mechanisms of link fading. We have shown that with the introduction of appropriate correlations, e.g., due to geographic distance, this model can be generalized to multiplex interactions. In case of a multiplicity of individual features serving as the basis of homophily, we have identified a transition between a segregated and a more heterogeneous phases, where the former is characteristic for critical situations, when only few features matter. In the paper we touch upon the problem of selecting communication channels as a limitation of the applicability of ICT data as a proxy for social interactions.
|Title of host publication||Pathways Between Social Science and Computational Social Science|
|Editors||Tamás Rudas, Gábor Péli|
|Publication status||Published - 2021|
|MoE publication type||A3 Book section, Chapters in research books|
|Name||Computational Social Sciences|