Overlapping community detection with a novel hybrid metaheuristic optimisation algorithm
by Imane Messaoudi; Nadjet Kamel
International Journal of Data Mining, Modelling and Management (IJDMMM), Vol. 12, No. 1, 2020

Abstract: Social networks are ubiquitous in our daily life. Due to the rapid development of information and electronic technology, social networks are becoming more and more complex in terms of sizes and contents. It is of paramount significance to analyse the structures of social networks in order to unveil the myth beneath complex social networks. Network community detection is recognised as a fundamental tool towards social networks analytics. As a consequence, numerical community detection methods are proposed in the literature. For a real-world social network, an individual may possess multiple memberships, while the existing community detection methods are mainly designed for non-overlapping situations. With regard to this, this paper proposes a hybrid metaheuristic method to detect overlapping communities in social networks. In the proposed method, the overlapping community detection problem is formulated as an optimisation problem and a novel bat optimisation algorithm is designed to solve the established optimisation model. To enhance the searchability of the proposed algorithm, a local search operator based on tabu search is introduced. To validate the effectiveness of the proposed algorithm, experiments on benchmark and real-world social networks are carried out. The experiments indicate that the proposed algorithm is promising for overlapping community detection.

Online publication date: Tue, 03-Mar-2020

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Mining, Modelling and Management (IJDMMM):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com