Community-driven network embedding via two-stage community structure refinement

Abstract

Community structure provides important mesoscopic information for network representation learning, yet most community-aware embedding methods use detected communities as fixed contexts or auxiliary regularizers. This paper proposes CDNE, a Community-Driven Network Embedding framework based on two-stage community structure refinement. In the first stage, CDNE applies the Louvain algorithm to obtain initial community partitions and designs a community-guided random walk strategy to sample both topological neighbors and same-community nodes. The generated sequences are used to learn preliminary node embeddings with the skip-gram model. In the second stage, these preliminary embeddings are clustered to refine community assignments, and the refined communities guide a new round of community-aware embedding learning. Through this design, CDNE incorporates local node proximity and mesoscopic community semantics without relying on an open-ended iterative procedure. Experiments on multiple real-world networks show that CDNE achieves strong performance in link prediction, cross-layer network reconstruction, and community-quality evaluation. Ablation analyses further indicate that meaningful community partitions, community-guided walks, and second-stage refinement jointly contribute to the observed improvements, suggesting that community refinement is an effective strategy for learning discriminative node representations.