Publication Type:

Conference Proceedings

Source:

2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Bangalore, India, India, p.pp. 866-872. (2018)

URL:

https://ieeexplore.ieee.org/document/8554522

Keywords:

clique percolation, Clustering algorithms, collaborative network, Collaborative work, community detection, Complex networks, edge streaming, Image edge detection, Merging, network graphs, social netwok, Social network services, Time complexity

Abstract:

Community structure prevails in network graphs like social networks, web graphs and collaborative networks. Clique percolation is one popular method used for unfolding the community structure in networks. However, clique percolation method is inefficient as the computational time is high for merging the identified cliques. This paper proposes a novel technique for detecting overlapping community structure by addressing the problem of clique merging. We reduce the overall time for community detection by applying edge streaming technique. The proposed method is validated through experiments using real and synthetic data in comparison with conventional clique percolation algorithm. The performance parameters such as execution time and goodness of the cluster are used for comparison and the results are promising. This model is suitable for community detection in collaborative network.

Cite this Research Publication

K. K. Samhitha, Dr. Sajeev G. P., and Jayasree Narayanan, “A Novel Community Detection Method for Collaborative Networks”, 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, Bangalore, India, India, pp. pp. 866-872., 2018.