Local Probabilistic Models for Link Prediction

By Chao Wang, Venu Satuluri, Srinivasan Parthasarathy. Industrial Conference on Data Mining, 2007

357 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/7d3cf8315cfb586dae984b0aa986ea0857c18cf0

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Citations in the corpus

Link prediction in social networks: the state-of-the-art (2014, methodology): “Wang et al. proposed a method which utilized three types of features, namely, co-occurrence probability features, topology features and semantic features, to solve the link prediction problem [57].”
Inferring social ties across heterogenous networks (2012, background): “There are also a few works which employ supervised approaches to predict links in social networks, such as [31, 2, 19].”
Link prediction in weighted networks: The role of weak ties (2010, background): “Some algorithms based on Markov chains [13] and machine learning [14] have been proposed recently, and another group of algorithms are based on the measurements of node similarity.”
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].”
Temporal Link Prediction Using Matrix and Tensor Factorizations (2010, result): “our data (0.05{0.07%). According to Liben-Nowell and Kleinberg, Katz and its variants are among the best link predictors; this observation has been supported by other work as well [Huang et al. 2005; Wang et al. 2007]. We note that Wang, Satuluri and Parthasarathy [2007] use the truncated sum approx”
A Survey of Link Prediction in Social Networks (2011, methodology): “[58] used the Junction Tree algorithm as an inference mechanism.”
Link Prediction via Matrix Factorization (2011, background): “Both linear [33] and nonlinear [14,5] ) T x j , where v (1) = v (2) iﬀ the graph is undirected.”
A Survey of Link Prediction in Complex Networks (2016, methodology): “…Markov Random Fields using Cx,y and Rx,y; while not M satisfies all constrains in Rx,y do for each r in Rx,y do Update M to force satisfying r; end end Px,y = Infer probability of ex,y from M; end scalable probabilistic method based on local topological features of the network [Wang et al. 2007].”
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.”
New perspectives and methods in link prediction (2010, background): “Additionally, both of these works employ semantic and contextual information that pertains almost exclusively to the bibliographic domain.”