An important problem in network science is finding relevant community structures in complex networks. A community structure is a partition of the network nodes into clusters or modules, such that each cluster is densely connected. Current community detection algorithms have time complexity, centralization, and scalability issues. In this paper, to solve this problem, we implement a multi-agent reinforcement learning algorithm that optimizes a quality metric known as modularity. We model each node of the network as an autonomous agent that can choose other nodes to form a cluster with. They receive a reward and learn a policy that maps actions to their values. Experiments on known real-world networks show results similar to other modularity optimization methods while providing answers for decentralization, data privacy, and scalability.
EC Paim, ALC Bazzan, C Chira, Detecting Communities in Networks: a Decentralized Approach Based on Multiagent Reinforcement Learning, 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2225-2232