Detection of four-node motif in complex networks

Title: Detection of four-node motif in complex networks
Creator: Ning, Zhaolong; Liu, Lei; Yu, Shuo; Xia, Feng
Date: 2018
Type: Text; Conference proceedings
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/183883
Identifier: vital:16392
Identifier: https://doi.org/10.1007/978-3-319-72150-7_37
Identifier: ISBN:978-3-319-72150-7
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.
Publisher: Springer International Publishing
Relation: Complex Networks & Their Applications VI; Lyon, France; November 29th-1st December, 2017 p. 453-462
Rights: All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Subject: Complex networks; Network models; Network dynamics; Network analysis
Reviewed