SGPD Volume Maximization for Community Detection

TL;DR

This paper explores a novel approach to community detection in complex networks by leveraging the topological roles of peripheral vertices, demonstrating its potential on real-world data.

Contribution

It introduces a new perspective on clustering by focusing on pseudo-peripheral vertices' roles in community structure, independent of node attributes.

Findings

Pseudo-peripheral vertices effectively identify community boundaries

Method shows promising results on real-world networks

Provides a new topological framework for community detection

Abstract

In this note we briefly study the feasibility of community detection in complex networks using peripheral vertices. Our method suggests a novel direction in axiomizing the problem of clustering in graphs and complex networks by looking at the topological role each vertex plays in the community structure, regardless of the attributes. The promising strength of pseudo-peripheral vertices as a lever for analysis of complex networks is also demonstrated on real-world data.