A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions

35 Pages Posted: 30 Oct 2015 Last revised: 10 Nov 2015

See all articles by Zhepeng Li

Zhepeng Li

Schulich School of Business, York University

Xiao Fang

Lerner College of Business and Economics, University of Delaware

Olivia R. Liu Sheng

University of Utah - David Eccles School of Business

Date Written: October 28, 2015

Abstract

Link recommendation has attracted significant attentions from both industry practitioners and academic researchers. In industry, link recommendation has become a standard and most important feature in online social networks, prominent examples of which include “People You May Know” on LinkedIn and “You May Know” on Google . In academia, link recommendation has been and remains a highly active research area. This paper surveys state-of-the-art link recommendation methods, which can be broadly categorized into learning-based methods and proximity-based methods. We further identify social and economic theories, such as social interaction theory, that underlie these methods and explain from a theoretical perspective why a link recommendation method works. Finally, we propose to extend link recommendation research in several directions that include utility-based link recommendation, diversity of link recommendation, link recommendation from incomplete data, and experimental study of link recommendation.

Keywords: Link Recommendation, Link Prediction, Social Network, Network Formation

Suggested Citation

Li, Zhepeng and Fang, Xiao and Sheng, Olivia R. Liu, A Survey of Link Recommendation for Social Networks: Methods, Theoretical Foundations, and Future Research Directions (October 28, 2015). Available at SSRN: https://ssrn.com/abstract=2682561 or http://dx.doi.org/10.2139/ssrn.2682561

Zhepeng Li

Schulich School of Business, York University ( email )

Toronto, Ontario
Canada

Xiao Fang (Contact Author)

Lerner College of Business and Economics, University of Delaware ( email )

Newark, DE 19716
United States

Olivia R. Liu Sheng

University of Utah - David Eccles School of Business ( email )

1645 E Campus Center Dr
Salt Lake City, UT 84112-9303
United States
801-585-9071 (Phone)

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