论文标题
深图聚类通过共同信息最大化和混合模型
Deep Graph Clustering via Mutual Information Maximization and Mixture Model
论文作者
论文摘要
归因的图形群集或社区检测，该检测学会群集图形的节点是图形分析中的一项艰巨的任务。在本文中，我们引入了一个对比的学习框架，用于学习群友好的节点嵌入。尽管图对比度学习在自我监督的图形学习中表现出了出色的表现，但是将其用于图形聚类并未得到很好的探索。我们提出了高斯混合物信息最大化（GMIM），该信息利用互信息最大化方法来嵌入节点。同时，它假设表示空间遵循高斯（MOG）分布的混合物。我们目标的聚类部分试图适合每个社区的高斯分布。在统一框架中，与MOG的参数共同优化了节点嵌入。现实世界数据集的实验证明了我们方法在社区检测中的有效性。
Attributed graph clustering or community detection which learns to cluster the nodes of a graph is a challenging task in graph analysis. In this paper, we introduce a contrastive learning framework for learning clustering-friendly node embedding. Although graph contrastive learning has shown outstanding performance in self-supervised graph learning, using it for graph clustering is not well explored. We propose Gaussian mixture information maximization (GMIM) which utilizes a mutual information maximization approach for node embedding. Meanwhile, it assumes that the representation space follows a Mixture of Gaussians (MoG) distribution. The clustering part of our objective tries to fit a Gaussian distribution to each community. The node embedding is jointly optimized with the parameters of MoG in a unified framework. Experiments on real-world datasets demonstrate the effectiveness of our method in community detection.
