Structural transition in social networks: The role of homophily

Yohsuke Murase, Hang Hyun Jo, János Török, János Kertész, Kimmo Kaski

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Abstract

We introduce a model for the formation of social networks, which takes into account the homophily or the tendency of individuals to associate and bond with similar others, and the mechanisms of global and local attachment as well as tie reinforcement due to social interactions between people. We generalize the weighted social network model such that the nodes or individuals have F features and each feature can have q different values. Here the tendency for the tie formation between two individuals due to the overlap in their features represents homophily. We find a phase transition as a function of F or q, resulting in a phase diagram. For fixed q and as a function of F the system shows two phases separated at F c . For F < F c large, homogeneous, and well separated communities can be identified within which the features match almost perfectly (segregated phase). When F becomes larger than F c , the nodes start to belong to several communities and within a community the features match only partially (overlapping phase). Several quantities reflect this transition, including the average degree, clustering coefficient, feature overlap, and the number of communities per node. We also make an attempt to interpret these results in terms of observations on social behavior of humans.

Original languageEnglish
Article number4310
JournalScientific Reports
Volume9
Issue number1
DOIs
StatePublished - 1 Dec 2019

Bibliographical note

Funding Information:
Y.M. 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.-H.J. 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). K.K. acknowledges the Rutherford Foundation Visiting Fellowship at The Alan Turing Institute, UK. The systematic simulations in this study were assisted by OACIS. Y.M., H.-H.J., J.T., and J.K. are thankful for the hospitality of Aalto University.

Publisher Copyright:
© 2019, The Author(s).

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