Detection of four-node motif in complex networks

Abstract

Complex network analysis has gained research interests in a wide range of fields. Network motif, which is one of the most popular network properties, is a statistically significant network subgraph. In this paper, we propose a fast methodology, called Four-node Motif Detection Algorithm (FMDA), to extract four-node motifs in complex networks. Specifically, we employ a two-way spectral clustering method to cut big networks into small sub-graphs, and then identify motifs by recognition algorithm to reduce the computational complexity. After that, we use three isomorphic four-node motifs to analyze network structure by American Physical Society (APS) data set.