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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