Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks. Although Graph Contrastive Learning has demonstrated promising performance, existing methods often suffer from the"structural isolation"issue during mini-batch training, making it challenging to capture cohesive community structures that characterize the global topological distribution. To address these challenges, we propose SCISE, a Scalable unsupervised graph Clustering framework that preserves structural Integrity by synergizing community-aware sampling with constrained Structural Entropy. Specifically, we first introduce the Structural Entropy Community Constraint operator (SECC), which optimizes structural information within a constrained solution space to mitigate community fragmentation and enhance partition cohesion. Second, to prevent global information loss during batch training, we design a Community-Aware Sampling Expansion (CSampE) mechanism that incorporates the community context of target nodes into sampling batches, effectively breaking structural barriers and preserving topological integrity. Finally, we devise a Structural Contrastive Learning (StructCL) module that refines edge weights based on intra-batch structural similarity, guiding the encoder to learn representations in a higher-order structural space. Extensive experiments on six mainstream benchmark datasets demonstrate that SCISE significantly outperforms state-of-the-art algorithms, with ablation studies and robustness analyses further validating its effectiveness and reliability for real-world large-scale graphs.
Jingyun Zhang, Hao Peng, Jianxin Li et al.· 0 citations
Directed hypergraphs have gained increasing attention for modeling group interactions while preserving directionality. However, link prediction in directed hypergraphs has rarely been studied despite its practical significance in complex systems analysis. Existing models perform poorly due to three major challenges in directed hypergraphs: 1) lacking effective feature initialization methods; 2) neglecting to detect higher order substructures; and 3) failing to capture long-range dependencies among vertices. To address these challenges, we propose a novel directed hypergraph motif-based neural network (DHMNN) for directed hyperlink prediction, which simultaneously captures higher order structural and connectivity information from the directed hypergraph topology. First, we introduce directed hypergraph motifs (DH-motifs) to explore higher order neighborhoods, analyzing vertex structural equivalence and generating structural features. Secondly, we utilize hypergraph incidence matrices to measure local connectivity, quantifying vertex co-occurrences and producing connectivity features. Then, we employ hypergraph attention to refine the vertex features at both global and local levels, further capturing long- and short-range dependencies. Finally, a new scoring layer is designed to assess the reliability of each link, considering its local properties, feature variance, and directionality. Extensive experiments on seven metabolic networks and three social networks demonstrate that DHMNN significantly and consistently outperforms state-of-the-art models, achieving a 3.40%-9.90% increase in accuracy. Our code is available at: https://github.com/XihangMeng/DHMNN.
Xihang Meng, Hao Peng, Guangjie Zeng et al.· IEEE Transactions on Neural...· 0 citations
A novel heuristic community detection algorithm, termed CoDeSEG, which identifies communities by minimizing the network's two-dimensional structural entropy within a potential game framework, and introduces a structural entropy-based node overlapping heuristic for detecting overlapping communities, with a near-linear time complexity.