DAETG: Dual-Stage Attention Embedding for Community Detection on Textual-Edge Graphs

Abstract

Textual-edge graphs represent interactions through both connectivity and relation-specific text. Learning community structure from these graphs requires an encoder that preserves the relevance of individual relations when combining semantic and structural information. We propose Dual-Stage Attention Embedding for Community Detection on Textual-Edge Graphs (DAETG). The first attention stage summarizes incident-edge text, and the second transfers its coefficients into neighboring-node aggregation as a learnable prior. Following topology-reconstruction pretraining, Kullback–Leibler self-training and differentiable modularity jointly refine the encoder and a centroid-based community assignment. Training uses fixed node and edge text embeddings and a supplied community count, without category-membership supervision. Experiments on four benchmark graphs with base and large Bidirectional Encoder Representations from Transformers (BERT) embeddings show competitive community-detection performance, with modest mean improvements that vary across datasets and evaluation metrics. Ablation studies on two graphs support the contributions of correctly associated edge text, the dual-stage encoder, and joint community refinement. Together, these results support using relation-specific text alongside topology to learn community representations.