AnySCAN: An Efficient Anytime Framework with Active Learning for Large-Scale Network Clustering

Network clustering is an essential approach to finding latent clusters in real-world networks. As the scale of real-world networks becomes increasingly larger, the existing network clustering algorithms fail to discover meaningful clusters efficiently. In this paper, we propose a framework called AnySCAN, which applies anytime theory to the structural clustering algorithm for networks (SCAN). Moreover, an active learning strategy is proposed to advance the refining procedure in AnySCAN framework. AnySCAN with the active learning strategy is able to find the exactly same clustering result on large-scale networks as the original SCAN in a significantly more efficient manner. Extensive experiments on real-world and synthetic networks demonstrate that our proposed method outperforms existing network clustering approaches.