Reduced network extremal ensemble learning (RenEEL) scheme for community detection in complex networks

TL;DR

This paper presents RenEEL, an ensemble learning method that iteratively refines community detection in complex networks by focusing on core groups, leading to improved modularity optimization.

Contribution

The paper introduces a novel Extremal Ensemble Learning scheme that enhances community detection efficiency and accuracy by iteratively reducing network size based on core groups.

Findings

Outperforms existing methods in maximizing modularity

Efficiently detects community structures in benchmark networks

Reduces computational complexity through network reduction

Abstract

We introduce an ensemble learning scheme for community detection in complex networks. The scheme uses a Machine Learning algorithmic paradigm we call Extremal Ensemble Learning. It uses iterative extremal updating of an ensemble of network partitions, which can be found by a conventional base algorithm, to find a node partition that maximizes modularity. At each iteration, core groups of nodes that are in the same community in every ensemble partition are identified and used to form a reduced network. Partitions of the reduced network are then found and used to update the ensemble. The smaller size of the reduced network makes the scheme efficient. We use the scheme to analyze the community structure in a set of commonly studied benchmark networks and find that it outperforms all other known methods for finding the partition with maximum modularity.