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A Proximity Based Community Detection in Temporal Graphs

With very fast global instant messaging services, communication networks of today are changing rapidly. To accurately represent such dynamic networks, a special type of a graph, known as temporal graph is indispensable. Majority of the community detection algorithms are designed for static graphs. So, an efficient conversion of temporal graphs to static graphs, while retaining important temporal information, enables the use of any standard community detection algorithm easily. This paper proposes a novel community detection method by constructing static graphs from temporal networks. The proposed method is validated through experiments. Results show that our technique leads to considerable runtime reduction upon dealing with large graphs comprise multiple community structures.