Comparative Analysis of Community Detection Algorithms on Social Network

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.6 · Issue 9

Published

April 2018

Access

Open Access

Licence

CC BY-NC-SA 4.0

Priya Chaudhary Nisha Pandey Dinesh Singh

Networks are very important structures. As modern humans, we are surrounded by them every minute. Therefore, the detection and aggregation of data and users in communities in social networks are important and complex activities. In this paper, we consider a method of analyzing the network, which is known as community detection. The detection of the community can be useful for identifying communities of common interests, which would be done for the benefit of the youth so that they are involved in things that interest them. There are several types of networks for community identification, like social networks and biological networks. Several different approaches have been proposed to solve the problem and one of these is the Louvain method based on maximality of modularity.
As the social networks evolve, the network community structure changes. How can the community structure be updated efficiently? In this, we provide two methods based on the Louvain algorithm, to determine the community structure to update, that is the Edge-distribution-Analysis Algorithm, this decides and adds of new edges, modularity-change algorithms analyze the rate of modularity and provides whether an update is necessary.

Priya Chaudhary, Nisha Pandey, Dinesh Singh (2018). Comparative Analysis of Community Detection Algorithms on Social Network. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.6, Issue 9. ISSN: 2319-4863.

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