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Social network structures play an important role in the lives of humans andnon-human animals by affecting wellbeing, the spread of disease andinformation, and evolutionary processes. Nevertheless, we still lack a goodunderstanding of how these structures emerge from individual behaviour. Here wepresent a general model for the emergence of social structures, which is basedon a key aspect of real social systems observed across species, namely socialpreferences for traits (individual characteristics such as age, sex, etc.). Wefirst show that the model can generate diverse artificial social structures,and consider its potential for being combined with real network data. We thenuse the model to gain fundamental insights into how two main categories ofsocial preferences (similarity and popularity) affect social structure andfunction. The results show that the types of social preference, in combinationwith the types of trait they are used with, can have important consequences forthe spread of information and disease, and the robustness of social structuresagainst fragmentation. The results also suggest that symmetric degreedistributions could be expected to be common in social networks. Moregenerally, the study implies that trait-based social preferences can haveconsequences for social systems that go far beyond their effect on directbenefits from social partners. We discuss the implications of the results forsocial evolution.

Linking social network structure and function to social preferences