Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks
The identification of community structure in a social network is an important problem tackled in the literature of network analysis. There are many solutions to this problem using a static scenario, when facing a dynamic scenario some solutions may be adapted but others simply do not fit, moreover when considering the demand to analyze constantly growing networks. In this context, we propose an approach to the problem of community detection in dynamic networks based on a reinforcement learning strategy to deal with changes on big networks using a local optimization on the modularity score of the changed entities. An experiment using synthetic and real-world dynamic network data shows results comparable to static scenarios.
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Community Detectionreinforcement-learningReinforcement Learning (RL)Similar Papers 제목 키워드 기반
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