Reliable Node Similarity Matrix Guided Contrastive Graph Clustering

Abstract

Graph clustering, which involves the partitioning of nodes within a graph into disjoint clusters, holds significant importance for numerous subsequent applications. Recently, contrastive learning, known for utilizing supervisory information, has demonstrated encouraging results in deep graph clustering. This methodology facilitates the learning of favorable node representations for clustering by attracting positively correlated node pairs and distancing negatively correlated pairs within the representation space. Nevertheless, a significant limitation of existing methods is their inadequacy in thoroughly exploring node-wise similarity. For instance, some hypothesize that the node similarity matrix within the representation space is identical, ignoring the inherent semantic relationships among nodes. Given the fundamental role of instance similarity in clustering, our research investigates contrastive graph clustering from the perspective of the node similarity matrix. We argue that an ideal node similarity matrix within the representation space should accurately reflect the inherent semantic relationships among nodes, ensuring the preservation of semantic similarities in the learned representations. In response to this, we introduce a new framework, Reliable Node Similarity Matrix Guided Contrastive Graph Clustering (NS4GC), which estimates an approximately ideal node similarity matrix within the representation space to guide representation learning. Our method introduces node-neighbor alignment and semantic-aware sparsification, ensuring the node similarity matrix is both accurate and efficiently sparse. Comprehensive experiments conducted on $8$ real-world datasets affirm the efficacy of learning the node similarity matrix and the superior performance of NS4GC.

arXiv commentAccepted by IEEE Transactions on Knowledge and Data Engineering (TKDE)

References in corpus (20)

• Adam: A Method for Stochastic Optimization
• Modularity and community structure in networks
• The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
• Simplifying Graph Convolutional Networks
• Graph Contrastive Learning with Adaptive Augmentation
• Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks
• Pitfalls of Graph Neural Network Evaluation
• Structural Deep Clustering Network
• Graph Self-Supervised Learning: A Survey
• Deep Graph Contrastive Representation Learning
• Contrastive Multi-View Representation Learning on Graphs
• Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
• Variational Graph Normalized Auto-Encoders
• Graph Communal Contrastive Learning
• Graph Clustering with Graph Neural Networks
• A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
• Graph Condensation: A Survey
• BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection
• Graph Condensation for Inductive Node Representation Learning
• Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised Learning

Cited by in corpus (1)

• Clustering-Oriented Generative Attribute Graph Imputation