3.8 Proceedings Paper

A NOVEL APPROACH FOR ANALYZING THE SOCIAL NETWORK

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2015.04.202

Keywords

Big data; Graph Mining; Social Network Analysis

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

3.8
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available