Ensemble approaches for improving community detection methods
This is an incremental improvement for researchers and practitioners in network analysis, offering a new approach to enhance existing community detection methods.
The paper tackles the problem of improving community detection in networks by proposing an ensemble method that aggregates information from multiple community structures, and it shows that this method performs well with low computational complexity when applied to a stochastic algorithm.
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.