Network Community Detection on Metric Space

TL;DR

This paper introduces a novel approach for community detection in networks by transforming graphs into metric space points, enabling efficient and competitive identification of communities with reduced computational time.

Contribution

The paper proposes a new method that converts graphs into metric space points for community detection, demonstrating improved efficiency and competitive results over existing algorithms.

Findings

Competitive community detection results on multiple datasets

Reduced computational time compared to traditional methods

Effective transformation of graphs into metric space for analysis

Abstract

Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.