Data clustering with edge domination in complex networks

TL;DR

This paper introduces a novel edge domination-based dynamical system for community detection and data clustering in complex networks, demonstrating competitive performance across multiple datasets.

Contribution

It proposes a new edge domination model for clustering, extending dynamical systems to improve community detection in complex networks.

Findings

The proposed algorithm performs well with known number of clusters.

It was tested on 10 datasets with 6 different techniques.

Results indicate competitive clustering performance.

Abstract

This paper presents a model for a dynamical system where particles dominate edges in a complex network. The proposed dynamical system is then extended to an application on the problem of community detection and data clustering. In the case of the data clustering problem, 6 different techniques were simulated on 10 different datasets in order to compare with the proposed technique. The results show that the proposed algorithm performs well when prior knowledge of the number of clusters is known to the algorithm.