Journal
INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION AND CONVERGENCE (ICCC 2015)
Volume 48, Issue -, Pages 686-691Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2015.04.202
Keywords
Big data; Graph Mining; Social Network Analysis
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Massive datasets are becoming more prevalent. In this paper, we propose an algorithm to process a large symmetric matrix of billion scale graph in order to extract knowledge from graph dataset. For example, interesting patterns like the people who frequently visit your page and the most number of participating triangles can be obtained using the algorithm. These interesting patterns are discovered by computation of several eigen values and eigen vectors. The main challenge in analyzing the graph data are simplifying the graph, counting the triangles, finding trusses. These challenges are addressed in the proposed algorithm by using orthogonalization, parallelization and blocking techniques. The proposed algorithm is able to run on highly scalable MapReduce environment. we use a social network dataset (facebook approximately 2 to 7 TB of data) to evaluate the algorithm. we also show experimental results to prove that the proposed algorithm scale well and efficiently process the billion scale graph.
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