research-article

GraRep: Learning Graph Representations with Global Structural Information

Authors:

Qiongkai XuAuthors Info & Claims

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Pages 891 - 900

https://doi.org/10.1145/2806416.2806512

Published: 17 October 2015 Publication History

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.

We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.

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Cited By

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Lin JWan YXu JQi X(2025)Semantic graph neural network with multi-measure learning for semi-supervised classificationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109647140(109647)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109647
Wang HZhuang LDing YTiwari PLiang C(2025)EDDINet: Enhancing drug–drug interaction prediction via information flow and consensus constrained multi-graph contrastive learningArtificial Intelligence in Medicine10.1016/j.artmed.2024.103029159(103029)Online publication date: Jan-2025
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Index Terms

GraRep: Learning Graph Representations with Global Structural Information
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information systems applications

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cover image ACM Conferences

CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

October 2015

1998 pages

ISBN:9781450337946

DOI:10.1145/2806416

General Chairs:
James Bailey
The University of Melbourne
,
Alistair Moffat
The University of Melbourne
,
Program Chairs:
Charu C. Aggarwal
IBM
,
Maarten de Rijke
University of Amsterdam
,
Ravi Kumar
Google
,
Vanessa Murdock
Microsoft
,
Timos Sellis
RMIT University
,
Jeffrey Xu Yu
Chinese University of Hong Kong

Copyright © 2015 ACM.

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Published: 17 October 2015

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CIKM'15: 24th ACM International Conference on Information and Knowledge Management

October 18 - 23, 2015

Melbourne, Australia

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CIKM '15 Paper Acceptance Rate 165 of 646 submissions, 26%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Yang XCao XZhao JDuan YZhao F(2025)Uncertainty embedding of attribute networks based on multi-view information fusion and multi-order proximity preservationNeurocomputing10.1016/j.neucom.2024.129188620(129188)Online publication date: Mar-2025
https://doi.org/10.1016/j.neucom.2024.129188
Lin JWan YXu JQi X(2025)Semantic graph neural network with multi-measure learning for semi-supervised classificationEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109647140(109647)Online publication date: Jan-2025
https://doi.org/10.1016/j.engappai.2024.109647
Wang HZhuang LDing YTiwari PLiang C(2025)EDDINet: Enhancing drug–drug interaction prediction via information flow and consensus constrained multi-graph contrastive learningArtificial Intelligence in Medicine10.1016/j.artmed.2024.103029159(103029)Online publication date: Jan-2025
https://doi.org/10.1016/j.artmed.2024.103029
Wei S(2024)Multi-angle information aggregation for inductive temporal graph embeddingPeerJ Computer Science10.7717/peerj-cs.256010(e2560)Online publication date: 26-Nov-2024
https://doi.org/10.7717/peerj-cs.2560
Sheng JYang ZWang BChen Y(2024)Attribute Graph Embedding Based on Multi-Order Adjacency Views and Attention MechanismsMathematics10.3390/math1205069712:5(697)Online publication date: 27-Feb-2024
https://doi.org/10.3390/math12050697
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https://doi.org/10.3390/app14114946
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Jeong SKim JLee BLim S(2024)User tendency-based rating scaling in online trading networksPLOS ONE10.1371/journal.pone.029790319:4(e0297903)Online publication date: 16-Apr-2024
https://doi.org/10.1371/journal.pone.0297903
Chen YWang JZou QNiu MDing YSong JWang Y(2024)DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction predictionBMC Biology10.1186/s12915-024-02030-922:1Online publication date: 14-Oct-2024
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Alvarez-Mamani EDechant RBeltran-Castañón CIbáñez A(2024)Graph embedding on mass spectrometry- and sequencing-based biomedical dataBMC Bioinformatics10.1186/s12859-023-05612-625:1Online publication date: 2-Jan-2024
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