Exploring triad-rich substructures by graph-theoretic characterizations in complex networks

TL;DR

This paper introduces a novel graph-theoretic approach to detect metadata groups in complex networks by leveraging triad-rich substructures, outperforming existing algorithms especially in sparse network scenarios.

Contribution

The paper proposes a new triad-rich substructure and an algorithm DIVANC for effective detection of metadata groups, including overlapping ones, in complex networks.

Findings

DIVANC outperforms existing algorithms in detecting metadata groups

Effective in identifying sparse metadata groups

Validated on PPI, synthetic, and football networks

Abstract

One of the most important problems in complex networks is how to detect metadata groups accurately. The main challenge lies in the fact that traditional structural communities do not always capture the intrinsic features of metadata groups. Motivated by the observation that metadata groups in PPI networks tend to consist of an abundance of interacting triad motifs, we define a 2-club substructure with diameter 2 which possessing triad-rich property to describe a metadata group. Based on the triad-rich substructure, we design a DIVision Algorithm using our proposed edge Niche Centrality DIVANC to detect metadata groups effectively in complex networks. We also extend DIVANC to detect overlapping metadata groups by proposing a simple 2-hop overlapping strategy. To verify the effectiveness of triad-rich substructures, we compare DIVANC with existing algorithms on PPI networks, LFR synthetic networks and football networks. The experimental results show that DIVANC outperforms most other algorithms significantly and, in particular, can detect sparse metadata groups.