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

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.

References in corpus (7)

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• Modularity and community structure in networks
• Finding community structure in networks using the eigenvectors of matrices
• Resolution limit in community detection
• Community Structure in Jazz
• The many facets of community detection in complex networks
• Fast and accurate determination of modularity and its effect size