Identifying networks with common organizational principles. (3rd February 2018)

• Record Type: Journal Article
• Authors: Wegner, Anatol E; Ospina-Forero, Luis; Gaunt, Robert E; Deane, Charlotte M; Reinert, Gesine
• Editors: Higham, Desmond
• Is Part Of: Journal of complex networks. Volume 6:Number 6(2018)
• Page Start: 887
• Page End: 913
• Publication Date: 2018-02-03
• Subjects: networks -- network comparison -- machine learning -- earth mover's distance -- network topology; Numerical analysis -- Periodicals; Computer networks -- Periodicals; Social networks -- Periodicals; 518.05
• DOI: 10.1093/comnet/cny003
• Languages: English
• ISSNs: 2051-1310

Abstract

Abstract: Many complex systems can be represented as networks, and the problem of network comparison is becoming increasingly relevant. There are many techniques for network comparison, from simply comparing network summary statistics to sophisticated but computationally costly alignment-based approaches. Yet it remains challenging to accurately cluster networks that are of a different size and density, but hypothesized to be structurally similar. In this article, we address this problem by introducing a new network comparison methodology that is aimed at identifying common organizational principles in networks. The methodology is simple, intuitive and applicable in a wide variety of settings ranging from the functional classification of proteins to tracking the evolution of a world trade network.