Statistical Relational Learning for Link Prediction

By Alexandrin Popescul, Lyle Ungar. 2003

302 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/67dc9b7c747436f9c8e74813d0ad4098428b2647

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Citations in the corpus

A statistical approach to rule learning (2006, methodology): “Structural logistic regression (Popescul et al., 2002; Popescul and Ungar, 2003) refines only those features already incorporated into the logistic regression model (g(q1, .”
Classification in Networked Data: A Toolkit and a Univariate Case Study (2007, unknown): “…as, De Raedt et al., 2001; Dzeroski and Lavrac, 2001; Kramer et al., 2001; Flach and Lachiche, 2004; Richardson and Domingos, 2006), or based on using relational database joins to generate relational features (e.g., Perlich and Provost, 2003; Popescul and Ungar, 2003; Perlich and Provost, 2006).”
A Survey of Link Prediction in Social Networks (2011, background): “The link prediction problem has also been studied previously in the context of relational data [53, 46, 47] and also in the Internet domain [50], where explicit graph representations were not used.”
Co-author Relationship Prediction in Heterogeneous Bibliographic Networks (2011, background): “As in [11], they study feature selection in a relational environment using relational languages, and feed these features into supervised link prediction models.”
Link prediction approach to collaborative filtering (2005, background): “Link prediction has been an important problem in network modeling and has recently been studied in social network, genetic interaction network, and literature citation network contexts [2, 5, 6].”
Supervised random walks: predicting and recommending links in social networks (2010, background): “Link prediction in supervised machine learning setting was mainly studied by the relational learning community [28, 26].”
Margin-based first-order rule learning (2007, background): “Popescul and Ungar (2003) extend Logistic Regression to the relational setting.”
Local Probabilistic Models for Link Prediction (2007, background): “The work of Popescul et al. [16] is another interesting approach to integrating different kinds of information.”