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A Novel Framework for Node/Edge Attributed Graph Embedding

  • Guolei SunEmail author
  • Xiangliang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11441)

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.

Keywords

Graph embedding Node/edge attributed graphs Network analysis 

Notes

Acknowledgement

This work is supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. 2639.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.King Abdullah University of Science and TechnologyThuwalSaudi Arabia

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