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

Zhepeng Li, Xiao Fang, Olivia Sheng
2015 arXiv   pre-print
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
more » ... egorized 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.
arXiv:1511.01868v1 fatcat:ff2fxkyosbgozed6lhahunw47a