Scalable Clustering Algorithm via a Triangle Folding Processing for Complex Networks

Ying Kang, Xiaoyan Gu, Weiping Wang, Dan Meng

Abstract: Facing up to the incessant growth of complex networks, more and more researchers start turning to a multilevel computing paradigm with high scalability for clustering. By virtue of iterative coarsening level by level, the clustering results which are obtained from the coarsest network and then projected to the original network, is superior to the ones from mining the original complex network explicitly. Empirical works reflect that the local-aggregation characteristic is a key point for multilevel clustering algorithms, thus techniques like modularity, label propagation etc. are used to discover the micro-clusters for coarsening. In this paper, we propose a scalable clustering algorithm via a triangle folding processing for complex networks(SCAFT). Based on the strong cluster property of triangle, we fold each traversed triangle of the network into a superverex to realize coarsening. And each generated coarsened network by iteration is capable of reserving the cluster structures of last level network, or even the intrinsic cluster structures of original complex network, improving the computational accuracy. What's more, a streaming algorithm is embedded in our novel approach to generate a serial input sequence of vertices, reducing the heavy burdens of memory usage of system. Experimental results on real-world complex networks show that, SCAFT outperforms the state-of-the-art multilevel clustering algorithms in terms of clustering accuracy, running time, especially in memory usage.