Fuzzy-Based Deep Attributed Graph Clustering

Attributed graph (AG) clustering is a fundamental, yet challenging, task for studying underlying network structures. Recently, a variety of graph representation learning models has been proposed to effectively infer the node embeddings, which are then incorporated into conventional clustering techniques to identify meaningful clusters. While these models tend to preserve node proximities, which reflect the similarity between nodes in both structural and attribute dimensions, for representation learning, they generally overlook the crucial dependencies between node embeddings and the resulting clusters. To overcome this problem, we propose a novel fuzzy-based deep AG clustering model, namely FDAGC, which is capable of achieving the task in a purely unsupervised and end-to-end manner without additionally incorporating conventional clustering techniques. In particular, FDAGC first encodes network structures and node attributes into a compact representation with graph convolution. A reconstruction error is then estimated to minimize the information loss during network message-passing. Besides, we utilize a self-monitoring training strategy to optimize node embeddings, thus improving the cluster cohesion by guiding them toward cluster centers. In the training phase, our expectations about resulting clusters are explicitly incorporated into the optimization of FDAGC via the concept of fuzzy clustering, thus leading to more accurate clustering by coupling the dependency between graph representation learning and AG clustering. Extensive experiments have demonstrated the superior performance of FDAGC in terms of several evaluation metrics, such as accuracy, normalized mutual information, F1-score and adjusted rand index, on six real-world AGs with different scales.