Incorporation of social features in the Naming Game for community detection

Abstract

The organization of individuals in groups or communities is an observed property of complex social networks and this structural organization emerges naturally due to the relationships built between people on a daily basis. We believe that the opinion exchange among individuals is a key factor to this community construction, given that sharing opinions bounds people together, and disagreeing constantly would probably weaken a relationship. In this work, we analyse three models of opinion exchange that uncover the community structure of a network, based on the Naming Game (NG), a classic model of linguistic interactions of agreement. The NG-based models applied in this work insert time-changing social features to the NG dynamics in order to form communities of nodes sharing different language conventions. For this matter, we explore the models NG-AW—that incorporates trust—, NG-LEF—that incorporates uncertainty—and NG-SM—finally incorporating opinion preference. We test the algorithms in LFR networks and show that the separate addition of each social feature in the Naming Game results in improvements in community detection. Our simulations show that opinions coexist at the end of the game in non-convergent executions, each name tagging a different community, identifying, by a socially guided language dynamics, the topological communities present on the network. Moreover, the resulting trust in edges and uncertainty in nodes classify them according to role and position in the network, respectively. We observed this behavior in large networks with disjoint communities generated using LFR benchmark, and we compared our results with existing results from the literature, focusing on the quality of the community detection per se. Our model with secondary memory has shown accuracy comparable with algorithms designed specifically for topological community detection, while modeling social features that reveal communities as an emergent property, as observed in real-world social systems.