Abstract：Graph clustering aims to partition graph nodes into different categories in an unsupervised manner, facilitating the discovery of hidden patterns, community structures and organizational relationships within complex systems. Existing methods construct different self-supervised information through various learning paradigms to guide graph representation learning and promote clustering. Therefore, the learning paradigm is the key to clustering algorithms. However, few existing reviews discuss graph clustering from the perspective of different learning paradigms. In this paper, the research progress on graph clustering based on different learning paradigms is summarized. Clustering methods are classified into reconstructive graph clustering, contrastive graph clustering, adversarial graph clustering and hybrid graph clustering. Considering the research scope and clustering effect, reconstructive graph clustering and contrastive graph clustering are discussed in detail. Graph clustering results on single-relation and multi-relation datasets are compared. The results show that contrastive graph clustering performs better on single-relation datasets, while reconstructive graph clustering is more effective on multi-relation datasets. Finally, the challenges faced in the graph clustering field are summarized, and future research directions are pointed out as well. The applications of deep graph clustering across various domains are additionally introduced.
周丽娟, 吴梦琪, 李欣冉, 牛常勇. 基于不同学习范式的深度图聚类方法综述[J]. 模式识别与人工智能, 2025, 38(3): 233-251.
ZHOU Lijuan, WU Mengqi, LI Xinran, NIU Changyong. A Survey of Deep Graph Clustering Methods Based on Different Learning Paradigms. Pattern Recognition and Artificial Intelligence, 2025, 38(3): 233-251.
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