A Novel Framework for Node/Edge Attributed Graph Embedding

Guolei Sun, Xiangliang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Graph embedding has attracted increasing attention due to its critical application in social network analysis. Most existing algorithms for graph embedding utilize only the topology information, while recently several methods are proposed to consider node content information. However, the copious information on edges has not been explored. In this paper, we study the problem of representation learning in node/edge attributed graph, which differs from normal attributed graph in that edges can also be contented with attributes. We propose GERI, which learns graph embedding with rich information in node/edge attributed graph through constructing a heterogeneous graph. GERI includes three steps: construct a heterogeneous graph, take a novel and biased random walk to explore the constructed heterogeneous graph and finally use modified heterogeneous skip-gram to learn embedding. Furthermore, we upgrade GERI to semi-supervised GERI (named SGERI) by incorporating label information on nodes. The effectiveness of our methods is demonstrated by extensive comparison experiments with strong baselines on various datasets.
Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining
PublisherSpringer Nature
Pages169-182
Number of pages14
ISBN (Print)9783030161415
DOIs
StatePublished - Mar 20 2019

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