Finding important edges in networks through local information

TL;DR

This paper introduces a new metric called subgraph overlap (SO) for identifying critical edges in complex networks, demonstrating superior performance over existing benchmarks in preserving network structure and function.

Contribution

The paper proposes the subgraph overlap (SO) metric that effectively quantifies edge importance considering community overlaps, advancing methods for critical edge detection.

Findings

SO outperforms benchmarks in identifying critical edges

Critical edges are key to network integrity and function

The method is applicable across various types of complex networks

Abstract

In transportation, communication, social and other real complex networks, some critical edges act a pivotal part in controlling the flow of information and maintaining the integrity of the structure. Due to the importance of critical edges in theoretical studies and practical applications, the identification of critical edges gradually become a hot topic in current researches. Considering the overlap of communities in the neighborhood of edges, a novel and effective metric named subgraph overlap (SO) is proposed to quantifying the significance of edges. The experimental results show that SO outperforms all benchmarks in identifying critical edges which are crucial in maintaining the integrity of the structure and functions of networks.