Ensemble approaches for improving community detection methods

TL;DR

This paper introduces an ensemble approach that combines multiple community detection results to enhance accuracy and robustness in identifying community structures within networks.

Contribution

It proposes a novel ensemble method for community detection that aggregates multiple detection results, improving performance with low computational cost.

Findings

The ensemble method outperforms individual algorithms on synthetic networks.

It achieves comparable or better accuracy with reduced computational complexity.

The approach is versatile, applicable to various community detection algorithms.

Abstract

Statistical estimates can often be improved by fusion of data from several different sources. One example is so-called ensemble methods which have been successfully applied in areas such as machine learning for classification and clustering. In this paper, we present an ensemble method to improve community detection by aggregating the information found in an ensemble of community structures. This ensemble can found by re-sampling methods, multiple runs of a stochastic community detection method, or by several different community detection algorithms applied to the same network. The proposed method is evaluated using random networks with community structures and compared with two commonly used community detection methods. The proposed method when applied on a stochastic community detection algorithm performs well with low computational complexity, thus offering both a new approach to community detection and an additional community detection method.