Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks in an unsupervised manner, has attracted great attention in recent years. Although the performance has been largely improved, the excellent performance of the existing methods heavily relies on an accurately predefined cluster number, which is not always available in the real-world scenario. To enable the deep graph clustering algorithms to work without the guidance of the predefined cluster number, we propose a new deep graph clustering method termed Reinforcement Graph Clustering (RGC). In our proposed method, cluster number determination and unsupervised representation learning are unified into a uniform framework by the reinforcement learning mechanism. Concretely, the discriminative node representations are first learned with the contrastive pretext task. Then, to capture the clustering state accurately with both local and global information in the graph, both node and cluster states are considered. Subsequently, at each state, the qualities of different cluster numbers are evaluated by the quality network, and the greedy action is executed to determine the cluster number. In order to conduct feedback actions, the clustering-oriented reward function is proposed to enhance the cohesion of the same clusters and separate the different clusters. Extensive experiments demonstrate the effectiveness and efficiency of our proposed method. The source code of RGC is shared at https://github.com/yueliu1999/RGC and a collection (papers, codes and, datasets) of deep graph clustering is shared at https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering on Github.

翻译：深度图聚类旨在通过神经网络以无监督方式将节点分组为不相交的聚类，近年来引起了广泛关注。尽管现有方法的性能已大幅提升，但其优异表现严重依赖于预先准确设定的聚类数，而在实际场景中这一条件往往难以满足。为使得深度图聚类算法无需预设聚类数也能正常工作，我们提出了一种新的深度图聚类方法，称为强化图聚类（RGC）。在该方法中，聚类数确定与无监督表示学习通过强化学习机制统一到同一框架下。具体而言，首先利用对比预训练任务学习具有判别性的节点表示；接着，为同时捕捉图中的局部与全局信息以准确获取聚类状态，我们同时考虑节点状态和聚类状态；随后，在每个状态下，通过质量网络评估不同聚类数的质量，并执行贪婪动作以确定聚类数。为实现反馈动作，我们提出了面向聚类的奖励函数，以增强同类簇的凝聚性并分离不同簇。大量实验证明了所提方法的有效性与高效性。RGC的源代码已共享于https://github.com/yueliu1999/RGC，深度图聚类的资料合集（论文、代码与数据集）已共享于https://github.com/yueliu1999/Awesome-Deep-Graph-Clustering (GitHub)。