  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.
