Attentive graph-based recursive neural network for collective vertex classification

Qiongkai Xu, Qing Wang, Chenchen Xu, Lizhen Qu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

4 Citations (Scopus)

Abstract

Vertex classification is a critical task in graph analysis, where both contents and linkage of vertices are incorporated during classification. Recently, researchers proposed using deep neural network to build an end-to-end framework, which can capture both local content and structure information. These approaches were proved effective in incorporating semantic meanings of neighbouring vertices, while the usefulness of this information was not properly considered. In this paper, we propose an Attentive Graph-based Recursive Neural Network (AGRNN), which exerts attention on neural network to make our model focus on vertices with more relevant semantic information. We evaluated our approach on three real-world datasets and also datasets with synthetic noise. Our experimental results show that AGRNN achieves the state-of-the-art performance, in terms of effectiveness and robustness. We have also illustrated some attention weight samples to demonstrate the rationality of our model.

Original languageEnglish
Title of host publicationCIKM '17
Subtitle of host publicationproceedings of the 2017 ACM on Conference on Information and Knowledge Management
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages2403-2406
Number of pages4
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Conference

Conference26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Country/TerritorySingapore
CitySingapore
Period6/11/1710/11/17

Keywords

  • Recursive Neural Network
  • Collective Vertex Classification
  • Attention Model

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