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Distributed detection of communities in complex networks using synthetic coordinates

Various applications like finding Web communities, detecting the structure of social networks, and even analyzing a graph’s structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, on the basis of local interactions between neighboring nodes and an unsupervised distributed hierarchical clustering algorithm. The novelty of the proposed approach, named SCCD (standing for synthetic coordinate community detection), lies in the fact that the algorithm is based on the use of Vivaldi synthetic network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that synthetic network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived from varying a number of graph parameters and real data set graphs. The experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme.