  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.
