Abstract
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.
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Notes
- 1.
The use of networks based on communication data as a proxy for the real social network has its limitations which we will discuss later.
- 2.
We call stylized facts the set of established properties, which have been found repeatedly as characteristic for social networks.
- 3.
Power-law tail of a distribution of a variable x means that for large values the distribution behaves like x −γ. As a power-law function is scale invariant, no characteristic scale occurs in it—hence the “scale free” terminology.
- 4.
Periodic boundary conditions: nodes on the left (top) side of the space are also close to the ones on the right (bottom) edge.
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Acknowledgements
Research reported here was supported by European grants FP7 SIMPLEX (JK), H2020 MULTIPLEX (JK), Hungarian grant OTKA K129124 “Uncovering patterns of social inequalities and imbalances in large-scale networks” (JK, JT), Aalto AScI (JT) the Academy of Finland (KK), The Alan Turing Institute (KK). YM acknowledges support from MEXT as “Exploratory Challenges on Post-K computer (Studies of multi-level spatiotemporal simulation of socioeconomic phenomena)” and from Japan Society for the Promotion of Science (JSPS) (JSPS KAKENHI; Grant No. 18H03621). H-HJ acknowledges financial support by Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Ministry of Education (NRF-2018R1D1A1A09081919). The systematic simulations in this study used OACIS (Murase 2014).
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Kertész, J., Török, J., Murase, Y., Jo, HH., Kaski, K. (2021). Modeling the Complex Network of Social Interactions. In: Rudas, T., Péli, G. (eds) Pathways Between Social Science and Computational Social Science. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-54936-7_1
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