Flow approaches to community detection in complex network systems

TL;DR

This paper introduces flow-based methods for community detection in complex networks, utilizing influence parameters and flow cores to identify communities where traditional methods struggle.

Contribution

It proposes two novel flow-based approaches and develops efficient algorithms that outperform existing methods in complex network community detection.

Findings

Flow influence parameters help identify communities.

Flow core concept enhances detection accuracy.

Algorithms outperform traditional methods in complex scenarios.

Abstract

The paper investigates the problem of finding communities in complex network systems, the detection of which allows a better understanding of the laws of their functioning. To solve this problem, two approaches are proposed based on the use of flows characteristics of complex network. The first of these approaches consists in calculating the parameters of influence of separate subsystems of the network system, distinguished by the principles of ordering or subordination, and the second, in using the concept of its flow core. Based on the proposed approaches, reliable criteria for finding communities have been formulated and efficient algorithms for their detection in complex network systems have been developed. It is shown that the proposed approaches make it possible to single out communities in cases in which the existing numerical and visual methods turn out to be disabled.