Ensemble approaches for improving community detection methods

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.

12 pages, 8 figures

References in corpus (1)

• Finding Community Structure in Mega-scale Social Networks

Cited by in corpus (5)

• Link-Prediction Enhanced Consensus Clustering for Complex Networks
• Community evolution in retweet networks
• Ensemble-Based Discovery of Disjoint, Overlapping and Fuzzy Community Structures in Networks
• YASCA: A collective intelligence approach for community detection in complex networks
• Ensemble-based Overlapping Community Detection using Disjoint Community Structures