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The number of community detection algorithms is growing continuously adopting a topological based approach to discover optimal subgraphs or communities. In this paper, we propose a new method combining both topology and semantic to evaluate and rank community detection algorithms. To achieve this goal we consider a probabilistic topic based approach to define a new measure called semantic divergence of communities. Combining this measure with others related to prior knowledge, we compute adoi:10.1109/scc.2016.101 dblp:conf/IEEEscc/NaimAQD16 fatcat:npufrbqrhzgu7ictbipuottunq