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：深度图聚类旨在利用深度神经网络将图中的节点划分为不相交的簇，近年来取得了显著进展。然而，现有方法难以扩展至包含百万级节点的大规模图。为解决这一问题，本文提出一种基于膨胀与收缩思想的可扩展深度图聚类方法（Dink-Net）。首先，通过判别节点是否被数据增强扰动，以自监督方式学习节点表示；同时，将聚类中心初始化为可学习的神经参数。随后，通过对抗性优化策略，最小化所提出的簇膨胀损失与簇收缩损失以优化聚类分布。基于上述设计，我们将表示学习与聚类优化这两个步骤统一为端到端框架，引导网络学习有利于聚类的特征。此外，由于所设计的损失函数采用小批量数据优化聚类分布且性能无衰减，Dink-Net可良好地扩展至大规模图。实验结果与理论分析均证明了该方法的优越性。相较于亚军方法，Dink-Net在包含1.11亿节点与16亿边的ogbn-papers100M数据集上实现了9.62%的NMI提升。源代码已发布于https://github.com/yueliu1999/Dink-Net。此外，深度图聚类的相关资源（论文、代码与数据集）已整理于https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering。