GraRep | RDL Research Database

Abstract

In this paper, we present {GraRep}, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al.

Keywords

Computer scienceGraphVertex (graph theory)Theoretical computer scienceArtificial intelligence

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Publication Info

Year: 2015
Type: article
Pages: 891-900
Citations: 1600
Access: Closed

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Cite This

APA Style

                            
                                    Shaosheng Cao, 
                                
                                    Wei Lu, 
                                
                                    Qiongkai Xu
                                
                            (2015). 
                            GraRep. 
                            
                            , 891-900.
                            https://doi.org/10.1145/2806416.2806512

Identifiers

DOI: 10.1145/2806416.2806512