Distance metric learning for complex networks: towards size-independent comparison of network structures.

Abstract

Real networks show nontrivial topological properties such as community structure and long-tail degree distribution. Moreover, many network analysis applications are based on topological comparison of complex networks. Classification and clustering of networks, model selection, and anomaly detection are just some applications of network comparison. In these… (More)
DOI: 10.1063/1.4908605

Topics

21 Figures and Tables

Cite this paper

@article{Aliakbary2015DistanceML, title={Distance metric learning for complex networks: towards size-independent comparison of network structures.}, author={Sadegh Aliakbary and Sadegh Motallebi and Sina Rashidian and Jafar Habibi and Ali Movaghar}, journal={Chaos}, year={2015}, volume={25 2}, pages={023111} }