Complex network representation through multi-dimensional node projection

TL;DR

This paper introduces a novel multi-dimensional node projection method inspired by signal transmission physics, capable of capturing complex network phenomena like scale-free and small-world properties, simplifying community detection.

Contribution

The paper presents a new network representation model using multi-dimensional signal spectrum projections, enabling easier community detection and analysis of complex networks.

Findings

Successfully reproduces scale-free and small-world properties

Enables community detection via clustering in projection space

Offers a general approach for representing arbitrary complex networks

Abstract

Complex network topology might get pretty complicated challenging many network analysis objectives, such as community detection for example. This however makes common emergent network phenomena such as scale-free topology or small-world property even more intriguing. In the present proof-of-concept paper we propose a simple model of network representation inspired by a signal transmission physical analogy, which is apparently capable of reproducing both of the above phenomena. The model appears to be general enough to represent and/or approximate arbitrary complex networks. We propose an approach constructing such a representation by projecting each node into a multi-dimensional space of signal spectrum vectors, where network topology is induced by their overlaps. As one of the implications this enables reducing community detection in complex networks to a straightforward clustering over the projection space, for which multiple efficient approaches are available. We believe such a network representation could turn out to be a useful tool for multiple network analysis objectives.