A Novel Reinforcement Learning Multi-Objective Community Detection Algorithm with $\epsilon$-Gradient-Greedy Strategy

Accurately categorizing communities within a social network is a crucial aspect of community detection, carrying significant practical relevance. To achieve a higher quality of community division, we combine reinforcement learning methods to learn the distribution characteristics of community nodes during the iteration process of the algorithm, which guides the nodes to migrate to other communities and enhances the algorithm's global search capability. Through a new <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\epsilon$</tex> -gradient-greedy strategy, which can obtain a node's gradient information relative to its neighbors and achieve higher performance in local search. To speed up the adaptability of the algorithm at the beginning of the iteration and to alleviate the resolution limitation imposed by modularity optimization, this paper employs a triangular subnetwork-based weight assignment method for balancing the weights of each edge in the network. Experimental results on real-world and synthetic network datasets demonstrate that our method's community identification precision outperforms recent community detection algorithms, exhibiting higher accuracy, higher resolution, and adaptability to various network characteristics and structural changes.