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Algorithms 2015, 8(3), 680-696; doi:10.3390/a8030680

Network Community Detection on Metric Space

Department of Computer Science and Engineering, Jaypee University of Information Technology, Waknaghat, Solan 173215, Himachal, India
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Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Received: 18 June 2015 / Revised: 15 August 2015 / Accepted: 19 August 2015 / Published: 21 August 2015
(This article belongs to the Special Issue Clustering Algorithms)
View Full-Text   |   Download PDF [158 KB, uploaded 21 August 2015]

Abstract

Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings. View Full-Text
Keywords: complex network; community detection; metric space complex network; community detection; metric space
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Saha, S.; Ghrera, S.P. Network Community Detection on Metric Space. Algorithms 2015, 8, 680-696.

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