A Survey of Link Recommendation for Social Networks

Abstract

Link recommendation has attracted significant attention 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 article 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.

Freedom and Constraint in Digital Environments: Implications for the Self

“…Similar to the need for research that actually alleviates privacy erosion, research must move beyond pointing out that algorithmic bias exists to developing and deploying algorithms that mitigate bias at scale. Researchers in industry and academia have long identified the lack of diversity and novelty provided by predictive algorithms as a potential problem (e.g., Z. Li et al, 2017; Terveen & McDonald, 2005; Vargas & Castells, 2011; Yu et al, 2009).…”

“…Researchers in industry and academia have long identified the lack of diversity and novelty provided by predictive algorithms as a potential problem (e.g., Z. Li et al, 2017;Terveen & McDonald, 2005;Vargas & Castells, 2011;Yu et al, 2009). One solution that engineers have used to meet consumer demand for heterogeneity and novelty is to program algorithms to occasionally recommend random or serendipitous content (Kotkov et al, 2016;Smets et al, 2022).…”

Missing link prediction and spurious link detection based on attractive force and community

“…Definition 4 (The attractive force between a node and a community) For node i and community c s in network N , the attractive force between them can be calculated by summing the attractive forces between node i and all the nodes connected to i in c s . It can be expressed by equation (2).…”

Directions for professional social matching systems

“…We argue that the experience of serendipity is an indicator of successful knowledge work, making it a desir-approaches for analyzing connections between individuals often utilize social network analysis and link prediction. 24 Following the triadic closure hypothesis, new ties are more likely to be formed between friends-of-friends or colleagues-of-colleagues, 10 that is, between actors that share a strong connection. The triadic closure mechanism can, however, enforce echo chambers and increase polarizationthe typical pitfalls of social networking services in the 2010s.…”

“…This objective differs fundamentally from the practices of straightforwardly predicting the likelihood of the formation of new social connections. 24 We point to Burt 7 who discussed social capital from two viewpoints: whether scarce or dense social networks produce social capital. Weak ties that gap structural holes can increase creativity and support the career development of those that occupy bridging roles.…”