Robust Contrastive Graph Clustering with Adaptive Local-Global Integration

• URL: http://arxiv.org/abs/2605.28209v2
• Date: Sat, 30 May 2026 02:28:25 GMT
• Title: Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
• Abstract summary: A contrastive graph clustering framework is proposed to jointly integrate multi-scale local structures with global semantics.<n> Experiments on eight real-world graph datasets demonstrate that our method achieves competitive clustering performance.
• Score: 10.185975874676995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities. Despite recent advances in self-supervised contrastive learning that have improved clustering via structural and attribute signals, existing methods still struggle to flexibly capture high-order local structures and often overlook global semantics in complex graphs. These limitations lead to suboptimal node representations, especially in real-world graphs with fragmented structures and ambiguous cluster boundaries. To address these limitations, a contrastive graph clustering framework is proposed to jointly integrate multi-scale local structures with global semantics via attention mechanisms. At the local level, GNN-based topological signals extracted from multiple propagation depths are adaptively fused through attention-based weighting to capture multi-scale neighborhood features. At the global level, semantic prototypes derived from dynamically evolving cluster centers are adaptively aggregated through attention to guide node representations and enhance inter-cluster separability. The model is trained under a dual-view contrastive learning paradigm with a hybrid objective that combines instance-level and structure-aware losses to improve representation robustness and discrimination. Experiments on eight real-world graph datasets demonstrate that our method achieves competitive clustering performance. Code is available at https://github.com/vege12138/w2.

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