Title: Hybrid feature-based approach for recommending friends in social networking systems

Authors: Rahul Kumar Yadav; Shashi Prakash Tripathi; Abhay Kumar Rai; Rajiv Ranjan Tewari

Addresses: Tata Consultancy Services, Synergy Park, Gachibowli, Hyderabad, Telangana 500032, India ' Tata Consultancy Services, Yantra Park, Pokharan Road Number 2, Thane West, Thane, Maharashtra 400606, India ' Department of Computer Science, Banasthali Vidyapith, Rajasthan 304022, India ' Centre of Computer Education, Institute of Professional Studies, University of Allahabad, Allahabad 211002, India

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.

Keywords: social networks; similarity measures; local graph features; global graph features; link prediction; hybrid approach; friend recommendations.

DOI: 10.1504/IJWBC.2020.105119

International Journal of Web Based Communities, 2020 Vol.16 No.1, pp.51 - 71

Received: 31 May 2019
Accepted: 08 Jun 2019

Published online: 07 Feb 2020 *

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