Abstract: Serving as a powerful tool for understanding intrinsic patterns and organizational structures within complex networks,dynamic community detection unveils the evolutionary process of densely connected sets of nodes,standing as a fundamental task in disciplines such as social sciences and urban planning.In recent years,various methods based on representation learning have been applied to the field of dynamic community detection.These methods map structured nodes into low-dimensional continuous latent space by integrating network topology and evolution characteristics,achieving reliable measurement of node similarity and difference.However,existing representation learning methods inadequately consider nodes' long-range information,falling short of capturing global structural features.To address this issue,this paper proposes DHM,which combines modularity optimization and hierarchical structure embedding to capture long-range node interactions.Specifically,DHM generates hierarchical organization based on the network's multi-granularity nature and embeds different levels of node relationships into node representations through bottom-up and top-down message-passing mechanisms.Experimental results on synthetic and real-world network datasets demonstrate that DHM outperforms existing dynamic community detection algorithms in terms of normalized mutual information,adjusted Rand index,and modularity,and can effectively detect communities in temporal networks.

Key words: Dynamic community detection, Dynamic networks, Modularity optimization, Hierarchical structure, Representation learning