research-article

SGCL: Contrastive Representation Learning for Signed Graphs

Authors:

Shaofeng ShenAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 1671 - 1680

https://doi.org/10.1145/3459637.3482478

Published: 30 October 2021 Publication History

Abstract

Graph contrastive representation learning aims to learn discriminative node representations by contrasting positive and negative samples. It helps models learn more generalized representations to achieve better performances on downstream tasks, which has aroused increasing research interest in recent years. Simultaneously, signed graphs consisting of both positive and negative links have become ubiquitous with the growing popularity of social media. However, existing works on graph contrastive representation learning are only proposed for unsigned graphs (containing only positive links) and it remains unexplored how they could be applied to signed graphs due to the distinct semantics and complex relations between positive and negative links. Therefore we propose a novel Signed Graph Contrastive Learning model (SGCL) to bridge this gap, which to the best of our knowledge is the first research to employ graph contrastive representation learning on signed graphs. Concretely, we design two types of graph augmentations specific to signed graphs based on a significant signed social theory, i.e., balance theory. Besides, inter-view and intra-view contrastive learning are proposed to learn discriminative node representations from perspectives of graph augmentations and signed structures respectively. Experimental results demonstrate the superiority of the proposed model over state-of-the-art methods on both real-world social datasets and online game datasets.

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Cited By

Minici MCinus FBonchi FManco GSerra ESpezzano F(2024)Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social BalanceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679786(1689-1699)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679786
Fang ZTan SWang Y(2024)A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328092435:10(14659-14670)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280924
Zhu YMichalak TLuo XZhang XZhou K(2024)Toward Secrecy-Aware Attacks Against Trust Prediction in Signed Social NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.336436619(3567-3580)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3364366
Show More Cited By

Index Terms

SGCL: Contrastive Representation Learning for Signed Graphs
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Learning latent representations
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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Published: 30 October 2021

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the Guangdong Basic and Applied Basic Research Foundation
UX Center, Netease Games
the Natural Science Foundation of Guangdong
the National Natural Science Foundation of China
the Key-Area Research and Development Program of Guangdong Provinc

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CIKM '21

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CIKM '21: The 30th ACM International Conference on Information and Knowledge Management

November 1 - 5, 2021

Queensland, Virtual Event, Australia

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

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Cited By

Minici MCinus FBonchi FManco GSerra ESpezzano F(2024)Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social BalanceProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679786(1689-1699)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679786
Fang ZTan SWang Y(2024)A Signed Subgraph Encoding Approach via Linear Optimization for Link Sign PredictionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.328092435:10(14659-14670)Online publication date: Oct-2024
https://doi.org/10.1109/TNNLS.2023.3280924
Zhu YMichalak TLuo XZhang XZhou K(2024)Toward Secrecy-Aware Attacks Against Trust Prediction in Signed Social NetworksIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.336436619(3567-3580)Online publication date: 2024
https://doi.org/10.1109/TIFS.2024.3364366
Qi YDu EShu LChen C(2024)SGCA: Signed Graph Contrastive Learning with Adaptive Augmentation2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651025(1-10)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651025
Zhou JLai YRen JZhou K(2024)Black-Box Attacks Against Signed Graph Analysis via Balance Poisoning2024 International Conference on Computing, Networking and Communications (ICNC)10.1109/ICNC59896.2024.10556322(530-535)Online publication date: 19-Feb-2024
https://doi.org/10.1109/ICNC59896.2024.10556322
Wang SNi LZhang ZLi XZheng XLiu J(2024)Multimodal prediction of student performance: A fusion of signed graph neural networks and large language modelsPattern Recognition Letters10.1016/j.patrec.2024.03.007181(1-8)Online publication date: May-2024
https://doi.org/10.1016/j.patrec.2024.03.007
Ko GJung J(2024)Learning disentangled representations in signed directed graphs without social assumptionsInformation Sciences: an International Journal10.1016/j.ins.2024.120373665:COnline publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120373
Sun HTian PXiong YZhang YXiang YJia XWang H(2024)DynamiSE: dynamic signed network embedding for link predictionMachine Language10.1007/s10994-023-06473-z113:7(4037-4053)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06473-z
Ko TChoi YKim CEvans RShpitser I(2023)Universal graph contrastive learning with a novel laplacian perturbationProceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence10.5555/3625834.3625938(1098-1108)Online publication date: 31-Jul-2023
https://dl.acm.org/doi/10.5555/3625834.3625938
Zhang ZLiu JZheng XWang YHan PWang YZhao KZhang Z(2023)RSGNN: A Model-agnostic Approach for Enhancing the Robustness of Signed Graph Neural NetworksProceedings of the ACM Web Conference 202310.1145/3543507.3583221(60-70)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583221
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