Learning Probabilistic Models of Link Structure

By Lise Getoor, Nir Friedman, Daphne Koller, B. Taskar. 2003

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Citations in the corpus

Using ghost edges for classification in sparsely labeled networks (2008, background): “In recent years, there has been a great deal of work on models for learning and inference in relational data [7, 10, 11, 16, 18].”
A Survey of Statistical Network Models (2009, background): “[117], Neville and Jensen [229]; Neville et al.”
Collective Classification in Network Data (2008, background): “This has been shown to work for some collective classification problems [12, 13], and has the advantage that a full joint distribution is defined.”
Combining link and content for community detection: a discriminative approach (2009, background): “Combining link and content for community detection usually achieves better performance, as revealed in studies [8, 11].”
Probabilistic Inductive Logic Programming (2004, background): “tic logic programs [30], learning of probabilistic relational models [ 18 ], parameter estimation of relational Markov models [60], learning of object-oriented Bayesian networks [3], learning relational dependency networks [44], and learning logic programs with annotated disjunctions [62, 51].”
FABIA: factor analysis for bicluster acquisition (2010, background): “Received on December 23, 2009; revised on March 26, 2010; accepted on April 20, 2010”
BLOG: Probabilistic Models with Unknown Objects (2005, background): “Basic PRMs (Koller & Pfeffer, 1998) have been extended in various ways, including number uncertainty—uncertainty about the number of objects standing in some relation to a single existing object—and existence uncertainty, such as uncertainty about whether there is a role for a given actor in a given”
Learning from labeled and unlabeled data on a directed graph (2005, background): “The World Wide Web can be thought of as a directed graph, in which the vertices represent web pages, and the directed edges hyperlinks. deal with structured data like the web (e.g. Getoor et al. (2002)).”
Hinge-Loss Markov Random Fields and Probabilistic Soft Logic (2015, background): “1 This idea has been explored for other classes of models, such as Markov logic networks (Richardson and Domingos, 2006) for discrete MRFs, relational dependency networks (Neville and Jensen, 2007) for dependency networks, and probabilistic relational models (Getoor et al., 2002) for Bayesian networ”
Leveraging Label-Independent Features for Classification in Sparsely Labeled Networks: An Empirical Study (2008, unknown): “, statistical relational learning or SRL) [3,4,5,6,7].”
Handling Missing Values when Applying Classification Models (2007, background): “…model trees (Landwehr et al., 2005), and as components of or tools for the development of graphical models such as Bayesian networks (Friedman and Goldszmidt, 1996), dependency networks (Heckerman et al., 2000), and probabilistic relational models (Getoor et al., 2002; Neville and Jensen, 2007).”
A Sound and Complete Algorithm for Learning Causal Models from Relational Data (2013, background): “Both RPC and PRMs include capabilities to reason about relationship existence (Getoor et al., 2002); however, we currently focus on attributional dependencies and leave causes of existence as future work.”