Incremental community miner for dynamic networks

Human relationships have led to complex communication networks among different individuals in a society. As the nature of relationship is change, these networks will change over the time too which makes them dynamic networks including several consecutive snapshots. Nowadays, the pervasiveness of electronic communication networks, so called Social Networks, has facilitated obtaining this valuable communication information and highlighted as one of the most interesting researchers in the field of data mining, called social network mining. One of the most challenging issues in the field of social network mining is community detection. It means to detect hidden communities in a social network based on the available information. This study proposes an appropriate solution to find and track communities in a dynamic social network based on the local information. Our approach tries to detect communities by finding initial kernels and maintaining them in the next snapshots. Using well-known datasets, the investigation and comparison of the proposed method with some state-of-the-art approaches indicates that the performance and computation complexity of our method is promising and can outperform its competitors.