Semi-Supervised Classification of Network Data Using Very Few Labels

By Frank Lin, William W. Cohen. 2010

116 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

DeepWalk: online learning of social representations (2014, neutral): “The most relevant relational classification algorithms to our work incorporate community information by learning clusters [33], by adding edges between nearby nodes [14], by using PageRank [24], or by extending relational classification to take additional features into account [43].”
Improving Distant Supervision for Information Extraction Using Label Propagation Through Lists (2015, methodology): “Label propagation methods (Zhu et al., 2003; Lin and Cohen, 2010) can be used to exploit this intuition, by propagating the low-conﬁdence labels associated with distance super-vision through an appropriate graph.”
Local Modeling of Attributed Graphs: Algorithms and Applications (2016, unknown): “The most relevant relational classification algorithms to our work incorporate community information by learning clusters [103], by adding edges between nearby nodes [49], by using PageRank [81], or by extending relational classification to take additional features into account [144].”
Discriminative Deep Random Walk for Network Classification (2016, methodology): “, 2008) propose a method that learn sparsely labeled network data by adding ghost edges between neighbor vertices, and (Lin and Cohen, 2010) by using PageRank.”
Structural Neighborhood Based Classification of Nodes in a Network (2016, unknown): “Multi Rank Walk [11], yet another relational classiﬁer, is based on the principle of random graph walks similar to Page Rank [16].”