Ensemble of Community Detection in Social Networks

One of the great challenges in Social Network Analysis (SNA) is community detection. Community is a group of vertices which have high intra connections and sparse inter connections. Community detection or Clustering reveals community structure of social networks and hidden relationships among their constituents. By considering the increase of datasets related to social networks, we need scalable algorithms to analyze these networks. Most of the community detection methods currently available are not deterministic and their results typically depend on specific random seeds or initial conditions. Ensemble clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. In this paper we propose an approach which goals in finding robust communities, with using ensemble community detection and we show that the proposed approach can be combined with any existing method in a self-consistent way, enhancing considerably the performance of the resulting partitions. The results of this study can be used for many issues such as more accurately identify community detection, marketing, advertising, networking and search engine optimization.