Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with deep neural networks, has achieved promising progress in recent years. However, the existing methods fail to scale to the large graph with million nodes. To solve this problem, a scalable deep graph clustering method (Dink-Net) is proposed with the idea of dilation and shrink. Firstly, by discriminating nodes, whether being corrupted by augmentations, representations are learned in a self-supervised manner. Meanwhile, the cluster centres are initialized as learnable neural parameters. Subsequently, the clustering distribution is optimized by minimizing the proposed cluster dilation loss and cluster shrink loss in an adversarial manner. By these settings, we unify the two-step clustering, i.e., representation learning and clustering optimization, into an end-to-end framework, guiding the network to learn clustering-friendly features. Besides, Dink-Net scales well to large graphs since the designed loss functions adopt the mini-batch data to optimize the clustering distribution even without performance drops. Both experimental results and theoretical analyses demonstrate the superiority of our method. Compared to the runner-up, Dink-Net achieves 9.62% NMI improvement on the ogbn-papers100M dataset with 111 million nodes and 1.6 billion edges. The source code is released at https://github.com/yueliu1999/Dink-Net. Besides, a collection (papers, codes, and datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering.

翻译：深度图聚类旨在通过深度神经网络将图的节点划分为不相交的聚类，近年来取得了显著进展。然而，现有方法无法扩展到具有百万节点的大规模图。为解决该问题，本文提出了一种基于扩张与收缩思想的可扩展深度图聚类方法（Dink-Net）。首先，通过区分节点是否被数据增强破坏，以自监督方式学习表示。同时，将聚类中心初始化为可学习的神经参数。随后，通过对抗性方式最小化所提出的聚类扩张损失和聚类收缩损失来优化聚类分布。通过这些设置，我们将表示学习和聚类优化这两步聚类过程统一为端到端框架，引导网络学习有利于聚类的特征。此外，Dink-Net 具有良好的大规模图扩展性，因为设计的损失函数采用小批量数据优化聚类分布，且性能不会下降。实验结果和理论分析均证明了我们方法的优越性。与亚军方法相比，Dink-Net 在具有1.11亿节点和16亿边的ogbn-papers100M数据集上实现了9.62% 的NMI提升。源代码已发布在 https://github.com/yueliu1999/Dink-Net。此外，深度图聚类的相关资源（论文、代码和数据集）整理在 https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering。