This paper studies the problem of graph-level clustering, which is a novel yet challenging task. This problem is critical in a variety of real-world applications such as protein clustering and genome analysis in bioinformatics. Recent years have witnessed the success of deep clustering coupled with graph neural networks (GNNs). However, existing methods focus on clustering among nodes given a single graph, while exploring clustering on multiple graphs is still under-explored. In this paper, we propose a general graph-level clustering framework named Graph-Level Contrastive Clustering (GLCC) given multiple graphs. Specifically, GLCC first constructs an adaptive affinity graph to explore instance- and cluster-level contrastive learning (CL). Instance-level CL leverages graph Laplacian based contrastive loss to learn clustering-friendly representations while cluster-level CL captures discriminative cluster representations incorporating neighbor information of each sample. Moreover, we utilize neighbor-aware pseudo-labels to reward the optimization of representation learning. The two steps can be alternatively trained to collaborate and benefit each other. Experiments on a range of well-known datasets demonstrate the superiority of our proposed GLCC over competitive baselines.
翻译:本文研究图层组群问题,这是一个新颖而又富有挑战性的任务。 这个问题在诸如生物信息学中的蛋白质组群和基因组分析等各种现实世界应用中至关重要。 近些年来,在与图形神经网络(GNNS)相结合的深度组群中取得了成功。 但是,现有的方法侧重于在给出单一图的节点组群中进行集群,同时探索多颗图集群集群集群集群集群集群集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集成集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集集