Publication Type:

Conference Paper

Source:

Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017, Institute of Electrical and Electronics Engineers Inc. (2017)

ISBN:

9781509038008

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046674937&doi=10.1109%2fICCSP.2017.8286442&partnerID=40&md5=a0e3868896c8a9b8c65bf035c0509310

Keywords:

Clustering algorithms, community detection, Complex networks, edge betweenness, Gn algorithms, Large-scale network, Map-reduce, Network properties, Real-world networks, Scalability, Signal processing, Single source shortest paths

Abstract:

In this paper, we propose a scalable method for finding the evolving communities in complex networks. The various network properties that are prominent in real-world networks are studied. The proposed algorithm computes the edge betweenness based on the transitive closure property combined with the greedy approach applied in Dijkstra's single source shortest path method. The major contribution is an improvement to GN algorithm in linear time for weighted undirected networks. The proposed algorithm is applied on Mapreduce to prove its scalability and enhance the performance in managing and analyzing the large scale networks. The experimentation of the distributed algorithm is tested on the private cluster and the results are as expected in the theoretical analysis. © 2017 IEEE.

Cite this Research Publication

R. G. Gayathri and Jyothisha J. Nair, “Mapreduce model for finding closely knit communities in large scale networks”, in Proceedings of the 2017 IEEE International Conference on Communication and Signal Processing, ICCSP 2017, 2017.