Structure-missing graph-level clustering network.

Graph-level clustering aims to group graphs into distinct clusters based on shared structural characteristics or semantic similarities. However, existing graph-level clustering methods generally assume that the input graph structure is complete and overlook the problem of missing relationships that commonly exist in real-world scenarios. These unmodeled missing relationships will lead to the accumulation of structural information distortion during the graph representation learning process, significantly reducing the clustering performance. To this end, we propose a novel method, Structure-Missing Graph-Level Clustering Network (SMGCN), which includes a structure augmentation module LR-SEA, an Anchor Positioning Mechanism, and Joint Contrastive Optimization. Specifically, we first output augmented graphs based on low-rank matrix completion, perform cluster matching using the Hungarian algorithm to obtain anchors, and then force same clustering graphs to converge to the corresponding anchors in the embedding space. According to our research, this is the first time that the graph-level clustering task with missing relations is proposed, and the superiority of our method is demonstrated through experiments on five benchmark datasets, compared with the state-of-the-art methods. Our source codes are available at https://github.com/MrHuSN/SMGCN.