紧致性与一致性：深度图聚类的联合框架
Compactness and Consistency: A Conjoint Framework for Deep Graph Clustering
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中文总结 AI 辅助
针对图神经网络学习节点表示的缺陷，提出联合框架CoCo，结合图卷积滤波器与一致性学习策略，在多数据集上实现优于现有方法的深度图聚类性能。
中文摘要 AI 辅助
图聚类是数据分析中的一项基础任务，旨在将图中具有相似特征的节点分组为簇。由于图神经网络（GNN）能够利用节点属性和图拓扑结构实现有效的簇分配，因此已被广泛用于解决该问题。然而，通过GNN学习到的表示通常难以通过局部消息传递机制捕捉节点间的全局关系，此外，图数据中固有的冗余和噪声可能导致节点表示缺乏紧致性和鲁棒性。为解决这些问题，我们提出了联合框架CoCo，用于深度图聚类，该框架可在学习到的节点表示中捕捉紧致性与一致性。从技术层面看，CoCo利用图卷积滤波器从局部和全局视图学习鲁棒的节点表示，随后将其编码为低秩紧致嵌入，从而有效去除冗余和噪声并揭示潜在的内在结构。为进一步丰富节点语义，我们基于紧致嵌入开发了一致性学习策略，以促进两种视角间的知识迁移。实验结果表明，在多个数据集上，我们的CoCo性能优于当前最先进的同类方法。
英文摘要
Graph clustering is a fundamental task in data analysis, aiming at grouping nodes with similar characteristics in the graph into clusters. This problem has been widely explored using graph neural networks (GNNs) due to their ability to leverage node attributes and graph topology for effective cluster assignments. However, representations learned through GNNs typically struggle to capture global relationships between nodes via local message-passing mechanisms. Moreover, the redundancy and noise inherently present in graph data may easily result in node representations lacking compactness and robustness. To address these issues, we propose a conjoint framework CoCo, which captures compactness and consistency in the learned node representations for deep graph clustering. Technically, our CoCo leverages graph convolutional filters to learn robust node representations from both local and global views, and then encodes them into low-rank compact embeddings, thus effectively removing the redundancy and noise as well as uncovering the intrinsic underlying structure. To further enrich the node semantics, we develop a consistency learning strategy based on compact embeddings to facilitate knowledge transfer from the two perspectives. Our experimental results indicate that our CoCo outperforms state-of-the-art counterparts on various datasets.
发表机构
- College of Computer Science, Sichuan University（四川大学计算机学院）
- Wellcome Sanger Institute（惠康桑格研究所）
- University of International Business and Economics（对外经济贸易大学）
- School of Computing and Information Technology, Great Bay University（湾区大学计算与信息技术学院）
- School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
- NITFID, School of Statistics and Data Science, Nankai University（南开大学统计与数据科学学院NITFID）
- College of Mathematics, Sichuan University（四川大学数学学院）
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