Self-supervised end-to-end graph local clustering

Graph clustering is a central and fundamental problem in numerous graph mining applications, especially in spatial-temporal system. The purpose of the graph local clustering is finding a set of nodes (cluster) containing seed node with high internal density. A series of works have been proposed to solve this problem with carefully designing the measuring metric and improving the efficiency-effectiveness trade-off. However, they are unable to provide a satisfying clustering quality guarantee. In this paper, we investigate the graph local clustering task and propose a End-to-End framework LearnedNibble to address the aforementioned limitation. In particular, we propose several techniques, including the practical self-supervised supervision manner with differential soft-mean-sweep operator, effective optimization method with regradient technique, and scalable inference manner with Approximate Graph Propagation (AGP) paradigm and search-selective method. To the best of our knowledge, LearnedNibble is the first attempt to take responsibility for the cluster quality and take both effectiveness and efficiency into consideration in an End-to-End paradigm with self-supervised manner. Extensive experiments on real-world datasets demonstrate the clustering capacity, generalization ability, and approximation compatibility of our LearnedNibble framework.