Community detection in dynamic networks with spark
by Priyangika R. Piyasinghe; J. Morris Chang
International Journal of Data Science (IJDS), Vol. 3, No. 3, 2018

Abstract: Detecting the evolution of communities within dynamically changing networks is important to understand the latent structure of complex large graphs. In this paper, we present an algorithm to detect real-time communities in dynamically changing networks. We demonstrate the proposed methodology through a case study in peer-to-peer (P2P) botnet detection which is one of the major threats to network security for serving as the infrastructure that is responsible for various cyber crimes. Our method considers online community structure from time to time and improves efficiency by maintaining the same level of accuracy of community detection over time. Experimental evaluation on Apache Spark implementation of the method showed that the execution time improves over dynamic version of Girvan-Newman community detection algorithm while having a higher accuracy level.

Online publication date: Tue, 04-Sep-2018

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