Home
This Title All WIREs
WIREs RSS Feed
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 2.111

Leader‐based community detection algorithm for social networks

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Community detection has become a crucial task in social network mining. Detecting communities summarizes interactions between members for gaining deep understanding of interesting characteristics shared between members of the same community. In this research, we propose a novel community detection algorithm for the purpose of revealing and analyzing hidden similar behavior of online users. The proposed algorithm is based mainly on similar members’ actions rather than the structure similarity only for the aim of detecting communities that are closely mapped to the underlying behavioral communities in real social networks. First, leaders of the social network are discovered, then, communities are detected based on those leaders. The idea is grounded on the assumption that communities could be formed around people with great influence. Extensive experiments and analysis show the ability of the proposed algorithm to successfully detect real‐world communities with improved accuracy.

Comparing accuracy for the proposed leader‐based community detection (LBCD) and the hierarchical diffusion algorithm (HDA).
[ Normal View | Magnified View ]
(a) No. of leaders at T = 1, 2, 3, 4 weeks and α = 2. (b) No. of leaders at T = 1, 2, 3, 4 weeks and α = 3.
[ Normal View | Magnified View ]
(a) No. of leaders at T = 1, 2, 3, 4 weeks and μ = 2. (b) No. of leaders at T = 1, 2, 3, 4 weeks and μ = 3.
[ Normal View | Magnified View ]
(a) Node degree distribution for the real social network dataset and (b) Flixster dataset.
[ Normal View | Magnified View ]
The proposed community detection algorithm.
[ Normal View | Magnified View ]
Illustrative example of social network graph.
[ Normal View | Magnified View ]
Comparing separability for proposed leader‐based community detection (LBCD) algorithm and the hierarchical diffusion algorithm (HDA).
[ Normal View | Magnified View ]
Comparing conductance for proposed leader‐based community detection (LBCD) algorithm and the hierarchical diffusion algorithm (HDA).
[ Normal View | Magnified View ]
(a) No. of leaders at T = 10, 20, 30, 60 days and α = 3 Flixster dataset. (b) No. of leaders at T = 10, 20, 30, 60 days and μ = 3 Flixster dataset.
[ Normal View | Magnified View ]
Comparing adjusted rand index (ARI) for the proposed leader‐based community detection (LBCD) and the hierarchical diffusion algorithm (HDA).
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Structure Discovery and Clustering

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts