Link Prediction in Relational Data

By B. Taskar, M. Wong, Pieter Abbeel, Daphne Koller. 2003

559 citations recorded by the source corpus.

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Citations in the corpus

A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “It has been shown that relational models which take the relationships of entities into account can signiﬁcantly outperform non-relational ML methods for this task (e.g., see [40, 41]).”
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].”
Residual Belief Propagation: Informed Scheduling for Asynchronous Message Passing (2006, background): “The network is induced by a relational Markov network (Taskar et al., 2004), which deﬁnes a set of template potentials.”
Relational Topic Models for Document Networks (2009, methodology): “Many models have been developed to explain network link structure (Wasserman and Pattison 1996; Newman 2002) and extensions which incorporate node attributes have been proposed (Getoor et al. 2001; Taskar et al. 2004).”
Iteratively clustering web images based on link and attribute reinforcements (2005, unknown): “The effectiveness of the third assumption---the reinforcements between intra-type links and inter-type links---has also been proved in [12][17] which point out that the existence of inter-type links " does depend on attributes of related objects, however one cannot reason directly about the groups…”
GMNN: Graph Markov Neural Networks (2019, methodology): “…is an important topic in machine learning, covering a variety of applications such as entity classification (Perozzi et al., 2014), link prediction (Taskar et al., 2004) and link 1Montréal Institute for Learning Algorithms (MILA) 2University of Montréal 3Canadian Institute for Advanced Research…”
Local Probabilistic Models for Link Prediction (2007, background): “Taskar et al. [18] use discriminatively trained relational Markov networks to define a joint probabilistic model over the entire graph (i.e. over the links as well as the content attributes of the nodes).”
Finding your friends and following them to where you are (2012, background): “In contrast with random walks, approaches related to our belief propagation method for enforcing and chaining soft transitive constraints have been validated in many areas in the machine learning literature, and are implicitly used in many works on link prediction as a way to solve the underlying pr”
Modeling relationship strength in online social networks (2010, background): “For example, researchers have found that network connectivity and attribute similarity can improve link prediction models [13, 19].”
Collective Classification in Network Data (2008, background): “Since then, collective classification has also been applied to various other applications such as part-of-speech tagging [33], classification of hypertext documents using hyperlinks [55], link prediction in friend-of-a-friend networks [56], optical character recognition [58], entity resolution in se”
Semi-supervised Penalized Output Kernel Regression for Link Prediction (2011, unknown): “The main approaches devoted to this task fall into two families: probabilistic graphical models (Miller et al., 2009; Taskar et al., 2003) provide posterior probabilities of links while kernel or similarity based methods take beneﬁt from the versatility of kernels to encode various structured…”
Link-Based Classification (2003, background): “This particular example (Taskar et al. [ 24 ]) also shows how a link-prediction problem can be posed as link-based classiflcation problem.”
The link prediction problem for social networks (2003, background): “The link prediction problem is also related to the problem of inferring missing links from an observed network: in a number of domains, one constructs a network of interactions based on observable data and then tries to infer additional links that, while not directly visible, are likely to exist [8,”
Link Prediction in Complex Networks: A Survey (2010, methodology): “Relational Markov Networks (RMNs) [113,114]: A RMN uses an undirected graph and a set of potential functions Φ to represent the joint distribution over the attributes of the item types.”