Detecting Overlapping Communities in Social Networks Using A Modified Segmentation by Weighted Aggregation Approach

Detecting communities of common behaviors, interests, and interactions in social networks is essential to model a network's structure. Overlapping community detection is an NP-Hard problem. Several solutions have been proposed; however, most of these techniques are computationally expensive. We have developed a fast-hierarchical algorithm using the notion of segmentation by weighted aggregation. Experimental results on synthetic and real benchmark networks show that the proposed algorithm effectively finds communities (Clusters) with varied overlap and non-exhaustiveness structures. Our method outperforms the state-of-the-art hierarchical clustering algorithms measured by the F-measure and the computational time.