A Survey of Link Prediction in Social Networks

By M. Hasan, Mohammed J. Zaki. Social Network Data Analytics, 2011

613 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/e7d30fefe1b99c21813873f976e46d03dc82b4fc

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Beyond the hype: Big data concepts, methods, and analytics (2015, positive): “In biology, link prediction techniques are used to discover links r associations in biological networks (e.g., protein–protein interction networks), eliminating the need for expensive experiments Hasan & Zaki, 2011).”
Recommender Systems (2016, neutral): “A survey on link-prediction methods for social network analysis may be found in [42].”
WTF: the who to follow service at Twitter (2013, methodology): “We adopt a standard formulation of user recommendation as a binary classification problem [10, 1].”
Deep Representation Learning for Social Network Analysis (2019, background): “The goal of link prediction is to infer the existence of new interactions or emerging links between users in the future, based on the observed links and the network evolution mechanism (Liben-Nowell and Kleinberg, 2007; Al Hasan and Zaki, 2011; Lü and Zhou, 2011).”
The Effect of Recommendations on Network Structure (2016, background): “For a thorough overview of this area, we point the interested reader to some relevant papers and surveys [1, 18, 19, 20], and the references therein.”
Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs (2017, methodology): “We cannot list all prior link prediction methods here and instead refer the reader to two survey papers [Nickel et al., 2016a, Al Hasan and Zaki, 2011].”
Cross View Link Prediction by Learning Noise-resilient Representation Consensus (2017, background): “Study on computer networks, biological and social networks has attracted great attention from the research community [7] [4] [26].”
Information diffusion in online social networks: a survey (2013, background): “A more complete review of literature on this topic can be found in [20].”
Graph Embedding Techniques, Applications, and Performance: A Survey (2017, methodology): “[53] survey the recent progress in this field and categorize the algorithms into (a) similarity based (local and global) [13], [14], [54], (b) maximum likelihood based [15], [16] and (c) probabilistic methods [17], [18], [55].”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “There are also several surveys on techniques for dynamic graphs [15, 254, 208, 2, 6], but they do not review recent advances in neural representation learning.”
Scalable Temporal Latent Space Inference for Link Prediction in Dynamic Social Networks (2014, background): “We refer readers to two recent survey papers [1], [2] and the related work section of the paper [44] for an exhaustive introduction to this thriving field.”
Link prediction in social networks: the state-of-the-art (2014, background): “There have already been several excellent survey works for the link prediction problem [102, 103, 104].”