Hybrid feature-based approach for recommending friends in social networking systems
by Rahul Kumar Yadav; Shashi Prakash Tripathi; Abhay Kumar Rai; Rajiv Ranjan Tewari
International Journal of Web Based Communities (IJWBC), Vol. 16, No. 1, 2020

Abstract: Link prediction is an effective technique to be applied on graph-based models due to its wide range of applications. It helps to understand associations between nodes in social communities. The social networking systems use link prediction techniques to recommend new friends to their users. In this paper, we design two time efficient algorithms for finding all paths of length-2 and length-3 between every pair of vertices in a network which are further used in computation of final similarity scores in the proposed method. Further, we define a hybrid feature-based node similarity measure for link prediction that captures both local and global graph features. The designed similarity measure provides friend recommendations by traversing only paths of limited length, which causes more faster and accurate friend recommendations. Experimental results show adequate level of accuracy in friend recommendations within considerable computing time.

Online publication date: Fri, 07-Feb-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 Web Based Communities (IJWBC):
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