Distributed community detection: Finding neighborhoods in a complex world using synthetic coordinates

In this paper, we propose an algorithm that finds the entire community structure of a network, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters.

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