Towards Modularity Optimization Using Reinforcement Learning to Community Detection in Dynamic Social Networks
This addresses the problem of analyzing constantly growing networks for researchers in network analysis, but it is incremental as it adapts existing methods to dynamic scenarios.
The paper tackles community detection in dynamic social networks by proposing a reinforcement learning approach that locally optimizes modularity for changed entities, achieving results comparable to static scenarios in experiments with synthetic and real-world data.
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.