Three-Way Decision-Driven Adaptive Graph Convolution for Deep Clustering

Graph clustering is an efficient method for deep clustering that utilizes graph convolution. Graph convolution effectively combines structure and content information, and lots of recent graph convolution-based methods have shown promising results in clustering performance on actual attribution networks. However, the established methods mainly employ a fixed graph convolution order, and limited studies have focused on the flexible choice of k-order graph convolution. When utilizing graph convolution with a fixed low order, only considering a few hops per node or neighbors within a set range of hops fails to maximize node relationships or account for the variations within the graphs. In this paper, we propose an adaptive method for graph clustering using a three-way decision idea. Our method enables the adaptive selection of k-order graph convolution for different graphs by searching for the k-order convolution kernel that best suits the subsequent clustering task. Additionally, our approach uses higher-order graph convolution to capture the global clustering structure. We assess the effectiveness of our approach through theoretical analysis and extensive experiments on benchmark datasets. Empirical evidence indicates that our method surpasses state-of-the-art techniques.