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Opinion exchange in a social network can profoundly affect its structure, given that agreeing with another person bounds he/she more closely and vice versa. In this work, we show that communication interactions can form and reveal the community structure of a network, as we present a new model where agents change their links and behaviors according to the local history of communication successes. Our simulations show that a local node parameter based on such history changes relatively to the node proportion of extra-community connections, and that (adaptive) edge weights tend to get high for intra-community and low for extra-community connections, also as a consequence of the history of communication successes. In non-convergent executions, the model gets trapped on a regime where clusters of agents agreeing with different words emerge, corresponding to the existing communities in the network.
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