Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection.
Complex social networks are often arranged in communities of agents playing similar roles in the network, and detecting these communities can bring insights into the behaviour of such systems. Among many existing methods, a model of communication dynamics that involves exchange and agreement on shared words -the Naming Game -has been applied for community detection based on local interactions. In a particular variation of this game, agents with simulated social features can produce, in non convergent executions, an emergent classification of nodes and edges according to their community-related positions in the network. In this work, we analyze and discuss more deeply this variation and propose a new model which includes a secondary memory that keeps a record of word occurrences, to better reveal the communities present in the network. Each agent in the network has a preference for communicating a given word from the primary memory according to its occurrences in the secondary memory, as a human would give preference for an opinion that he/she heard many times before. Our simulations show that not only there is great improvement in the detection of communities, but also in the probability of global non-convergence -necessary for guaranteeing different communities being tagged by different sets of shared words -and in the adequate classification of both edges and nodes in all networks generated using two of the most popular Community Detection benchmarks.
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