Identifying significant edges via neighborhood information

TL;DR

This paper introduces the second-order neighborhood (SN) index to identify significant edges in networks, demonstrating its superior performance over existing methods through extensive real-world network analysis.

Contribution

The paper proposes a novel SN index for edge significance and validates its effectiveness against benchmark methods on multiple real networks.

Findings

SN index outperforms benchmark methods in edge percolation tests

SN index effectively captures the importance of edges in network structure

The method is applicable to various real-world networks

Abstract

Heterogeneous nature of real networks implies that different edges play different roles in network structure and functions, and thus to identify significant edges is of high value in both theoretical studies and practical applications. We propose the so-called second-order neighborhood (SN) index to quantify an edge's significance in a network. We compare SN index with many other benchmark methods based on 15 real networks via edge percolation. Results show that the proposed SN index outperforms other well-known methods.