Abstract
Community detection in social networks is an important field of research in data mining and has an abundant literature. Time varying social networks require algorithms that can comply with temporal changes and are also feasible with limited resources. The performance of static algorithms are not well suited for such perturbing networks. Continuously updating community structure, light computations, on-demand results etc. are few of the new challenges introduced on account of dynamic networks. The aforementioned challenges are addressed in the proposed work. The work proposes a tree-based community detection in dynamic social networks (TCD2) algorithm which exploits two important properties of social network, connectedness and influence, for finding communities in the network. TCD2 uses a tree-structure to maintain the information of dynamically changing community structures of the network. The experimental results on real-world social networks along with synthetic networks validate the performance of TCD2. The tests also confirmed its superiority over the state-of-the-art algorithms. The results showed that the proposed algorithm achieves a significant trade-off between quality and accuracy.
Published Version
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