| Summary: | Analyzing social networks has received a lot of reviews in the recent literature. Many papers have been proposed to provide new techniques for mining social networks to help further study this huge amount of data. However, to the best of our knowledge, none of them considered the 'semantic meaning' of the nodes interests while clustering the network. In this work, we propose a new algorithm, namely GeoSim, for clustering users in any social network site into communities based on the 'semantic meaning' of the nodes interests as well as their relationships with each other. Moreover, this paper proposes a parallel version of the GeoSim algorithm that utilizes the MapReduce model to run on multiple machines simultaneously and get faster results. The two versions of the algorithm (centralized and parallel) are examined thoroughly to test their performance. The experiments show that both versions of the GeoSim algorithm achieve high community detection accuracy and scale linearly with the size of the cluster.
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