Link prediction using supervised learning

By M. Hasan, Vineet Chaoji, Saeed Salem, Mohammed J. Zaki. 2006

973 citations recorded by the source corpus.

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Citations in the corpus

What Does Explainable AI Really Mean? A New Conceptualization of Perspectives (2017, background): “…grounds, e.g. when they predict a person’s weight and health by their social media images [8] or the world region they are from [7] as part of a downstream determination about their future, like when they will quit their job [12], commit a crime [4], or could be radicalized into terrorism [1]?”
Temporal Link Prediction Using Matrix and Tensor Factorizations (2010, methodology): “We show how the well-known Katz method for link prediction can be extended to bipartite graphs and, moreover, approximated in a scalable way using a truncated singular value decomposition.”
Link Prediction Based on Graph Neural Networks (2018, background): “Moreover, link prediction is also studied as a supervised learning problem, where nodes’ explicit attributes are combined with some heuristics to train a supervised learning model [10].”
Friendship prediction and homophily in social media (2012, background): “Another line of works focuses on link detection through supervised learning methods trained on the topological features of graph [Popescul et al. 2003; Hasan et al. 2006].”
Inferring anchor links across multiple heterogeneous social networks (2013, unknown): “Copyright 2013 ACM 978-1-4503-2263-8/13/10 ...$15.00. http://dx.doi.org/10.1145/2505515.2505531.”
A Survey of Link Prediction in Social Networks (2011, background): “[22], the authors show that the summation of the neighbor-count of a pair of vertices is a very good attribute, which stands out as the second ranked feature in the link prediction task in a co-authorship network.”
Co-author Relationship Prediction in Heterogeneous Bibliographic Networks (2011, methodology): “Subsequently, supervised methods were proposed which combine different features with different coefficients via training data sets [4], [15], [8].”
Joint Link Prediction and Attribute Inference Using a Social-Attribute Network (2014, methodology): “Mapping link prediction to a classiﬁcation problem [Hasan et al. 2006; Lichtenwalter et al. 2010; Doppa et al. 2010] is another way to incorporate attributes.”
Dropout Training of Matrix Factorization and Autoencoder for Link Prediction in Sparse Graphs (2015, background): “MF+AE is also related to the supervised learning based methods [3, 13].”
Relational Deep Learning: A Deep Latent Variable Model for Link Prediction (2017, methodology): “They ignore the node attributes which are in fact also useful for link prediction (Al Hasan et al. 2006; Doppa et al. 2009; Hunter et al. 2011).”
A Survey of Link Prediction in Complex Networks (2016, background): “Some works have compared different classifiers including decision trees, support vector machines (SVMs), k-nearest neighbors, multilayer perceptrons, radial basis function networks, naive Bayes, and different ensembles of these classifiers [Al Hasan et al. 2006].”
Local Probabilistic Models for Link Prediction (2007, methodology): “Hasan et al. [6] have used topological features (such as the shortest distance between the two nodes), aggregated features (such as the sum of neighbors) and semantic features (such as the number of matching keywords).”
Link Prediction via Matrix Factorization (2011, methodology): “Both linear [33] and nonlinear [14,5] ) T x j , where v (1) = v (2) iﬀ the graph is undirected.”
Link prediction in social networks: the state-of-the-art (2014, unknown): “A learning-based approach is treating the link prediction problem as a binary classiﬁcation task [101].”