ABSTRACT
Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in real-world applications, Graph Contrastive Learning (GCL) is leveraged to train GNNs with limited or even no labels by maximizing the mutual information between nodes in its augmented views generated from the original graph. However, the distribution of graphs remains unconsidered in view generation, resulting in the ignorance of unseen edges in most existing literature, which is empirically shown to be able to improve GCL's performance in our experiments. To this end, we propose to incorporate graph generative adversarial networks (GANs) to learn the distribution of views for GCL, in order to i) automatically capture the characteristic of graphs for augmentations, and ii) jointly train the graph GAN model and the GCL model. Specifically, we present GACN, a novel Generative Adversarial Contrastive learning Network for graph representation learning. GACN develops a view generator and a view discriminator to generate augmented views automatically in an adversarial style. Then, GACN leverages these views to train a GNN encoder with two carefully designed self-supervised learning losses, including the graph contrastive loss and the Bayesian personalized ranking Loss. Furthermore, we design an optimization framework to train all GACN modules jointly. Extensive experiments on seven real-world datasets show that GACN is able to generate high-quality augmented views for GCL and is superior to twelve state-of-the-art baseline methods. Noticeably, our proposed GACN surprisingly discovers that the generated views in data augmentation finally conform to the well-known preferential attachment rule in online networks.
Supplemental Material
- 2016. UC Irvine messages network dataset - KONECT. http://konect.uni- koblenz.de/networks/opsahl-ucsocialGoogle Scholar
- Albert-László Barabási and Réka Albert. 1999. Emergence of scaling in random networks. science, Vol. 286, 5439 (1999), 509--512.Google Scholar
- S. Becker and G. E. Hinton. 1992. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, Vol. 355, 6356 (1992), 161.Google Scholar
- Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, and Stephan Günnemann. 2018. Netgan: Generating graphs via random walks. In International Conference on Machine Learning. PMLR, 610--619.Google Scholar
- Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, and Jie Tang. 2019. Representation Learning for Attributed Multiplex Heterogeneous Network. In KDD. ACM, 1358--1368.Google Scholar
- Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597--1607.Google Scholar
- Quanyu Dai, Qiang Li, Jian Tang, and Dan Wang. 2018. Adversarial network embedding. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 32.Google ScholarCross Ref
- Emily L Denton, Soumith Chintala, Rob Fergus, et al. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. Advances in neural information processing systems, Vol. 28 (2015).Google Scholar
- Hongchang Gao, Jian Pei, and Heng Huang. 2019. Progan: Network embedding via proximity generative adversarial network. In Proceedings of the 25th ACM SIGKDD International Conf. on Knowledge Discovery & Data Mining. 1308--1316.Google ScholarDigital Library
- C Lee Giles, Kurt D Bollacker, and Steve Lawrence. 1998. CiteSeer: An automatic citation indexing system. In Proceedings of the third ACM conference on Digital libraries. 89--98.Google ScholarDigital Library
- Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems, Vol. 27 (2014).Google ScholarDigital Library
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In KDD. ACM.Google Scholar
- Tiankai Gu, Chaokun Wang, Cheng Wu, Yunkai Lou, Jingcao Xu, Changping Wang, Kai Xu, Can Ye, and Yang Song. 2022. HybridGNN: Learning Hybrid Representation for Recommendation in Multiplex Heterogeneous Networks. In ICDE. IEEE, 1355--1367.Google Scholar
- William L. Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS.Google Scholar
- Kaveh Hassani and Amir Hosein Khasahmadi. 2020. Contrastive multi-view representation learning on graphs. In ICML. PMLR, 4116--4126.Google Scholar
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In The World Wide Web Conference. 507--517.Google ScholarDigital Library
- Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639--648.Google ScholarDigital Library
- Olivier Henaff. 2020. Data-efficient image recognition with contrastive predictive coding. In International Conference on Machine Learning. PMLR, 4182--4192.Google Scholar
- R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. In ICLR.Google Scholar
- Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. 2022. GraphMAE: Self-Supervised Masked Graph Autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD '22). Association for Computing Machinery, NY, USA, 594--604. https://doi.org/10.1145/3534678.3539321Google ScholarDigital Library
- Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019).Google Scholar
- Nikola Jovanović, Zhao Meng, Lukas Faber, and Roger Wattenhofer. 2021. Towards robust graph contrastive learning. arXiv preprint arXiv:2102.13085 (2021).Google Scholar
- Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).Google Scholar
- Kwot Sin Lee, Ngoc-Trung Tran, and Ngai-Man Cheung. 2021. Infomax-gan: Improved adversarial image generation via information maximization and contrastive learning. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. 3942--3952.Google ScholarCross Ref
- Kai Lei, Meng Qin, Bo Bai, Gong Zhang, and Min Yang. 2019. GCN-GAN: A non-linear temporal link prediction model for weighted dynamic networks. In IEEE INFOCOM 2019-IEEE Conference on Computer Communications. IEEE, 388--396.Google ScholarDigital Library
- Jiwei Li, Will Monroe, Tianlin Shi, Sébastien Jean, Alan Ritter, and Dan Jurafsky. 2017. Adversarial learning for neural dialogue generation. arXiv preprint arXiv:1701.06547 (2017).Google Scholar
- Ralph Linsker. 1988. Self-organization in a perceptual network. Computer, Vol. 21, 3 (1988), 105--117.Google ScholarDigital Library
- Weiyi Liu, Pin-Yu Chen, Fucai Yu, Toyotaro Suzumura, and Guangmin Hu. 2019. Learning graph topological features via GAN. IEEE Access, Vol. 7 (2019), 21834--21843.Google ScholarCross Ref
- Andrew Kachites McCallum, Kamal Nigam, Jason Rennie, and Kristie Seymore. 2000. Automating the construction of internet portals with machine learning. Information Retrieval, Vol. 3, 2 (2000), 127--163.Google ScholarDigital Library
- Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018).Google Scholar
- Tian Pan, Yibing Song, Tianyu Yang, Wenhao Jiang, and Wei Liu. 2021. Videomoco: Contrastive video representation learning with temporally adversarial examples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11205--11214.Google ScholarCross Ref
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In KDD. 701--710.Google ScholarDigital Library
- Yiwei Sun, Suhang Wang, Tsung-Yu Hsieh, Xianfeng Tang, and Vasant Honavar. 2019. Megan: A generative adversarial network for multi-view network embedding. arXiv preprint arXiv:1909.01084 (2019).Google Scholar
- Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial graph augmentation to improve graph contrastive learning. Advances in Neural Information Processing Systems, Vol. 34 (2021).Google Scholar
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding.. In WWW. ACM.Google ScholarDigital Library
- Sahar Tavakoli, Alireza Hajibagheri, and Gita Sukthankar. 2017. Learning social graph topologies using generative adversarial neural networks. In International Conference on Social Computing, Behavioral-Cultural Modeling & Prediction.Google Scholar
- Yonglong Tian, Dilip Krishnan, and Phillip Isola. 2020. Contrastive multiview coding. In European conference on computer vision. Springer, 776--794.Google ScholarDigital Library
- Aaron Van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv e-prints (2018), arXiv--1807.Google Scholar
- Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019. Deep Graph Infomax. ICLR (Poster), Vol. 2, 3 (2019), 4.Google Scholar
- Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, and Minyi Guo. 2018. Graphgan: Graph representation learning with generative adversarial nets. In Proceedings of the AAAI conference on artificial intelligence, Vol. 32.Google ScholarCross Ref
- Jun Wang, Lantao Yu, Weinan Zhang, Yu Gong, Yinghui Xu, Benyou Wang, Peng Zhang, and Dell Zhang. 2017. Irgan: A minimax game for unifying generative and discriminative information retrieval models. In Proceedings of the 40th International ACM SIGIR conference on Research and Development in Information Retrieval. 515--524.Google ScholarDigital Library
- Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang Song, Kai Zheng, Xiaowei Wang, and Guorui Zhou. 2023. Instant Representation Learning for Recommendation over Large Dynamic Graphs. In 2023 IEEE 39th International Conference on Data Engineering (ICDE). IEEE, 81--94.Google ScholarCross Ref
- Jiancan Wu, Xiang Wang, Fuli Feng, Xiangnan He, Liang Chen, Jianxun Lian, and Xing Xie. 2021. Self-supervised graph learning for recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. 726--735.Google ScholarDigital Library
- Longqi Yang, Liangliang Zhang, and Wenjing Yang. 2021. Graph adversarial self-supervised learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 14887--14899.Google Scholar
- Min Yang, Junhao Liu, Lei Chen, Zhou Zhao, Xiaojun Chen, and Ying Shen. 2019. An advanced deep generative framework for temporal link prediction in dynamic networks. IEEE transactions on cybernetics, Vol. 50, 12 (2019), 4946--4957.Google ScholarCross Ref
- Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. In International Conference on Machine Learning. PMLR, 12121--12132.Google Scholar
- Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems, Vol. 33 (2020), 5812--5823.Google Scholar
- Lantao Yu, Weinan Zhang, Jun Wang, and Yong Yu. 2017. Seqgan: Sequence generative adversarial nets with policy gradient. In Proceedings of the AAAI conference on artificial intelligence, Vol. 31.Google ScholarCross Ref
- Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, and Wei Wang. 2018. Learning deep network representations with adversarially regularized autoencoders. In Proceedings of the 24th ACM SIGKDD international conf. on knowledge discovery & data mining. 2663--2671.Google ScholarDigital Library
- Yuan Zhang, Regina Barzilay, and Tommi Jaakkola. 2017. Aspect-augmented adversarial networks for domain adaptation. Transactions of the Association for Computational Linguistics, Vol. 5 (2017), 515--528.Google ScholarCross Ref
- Han Zhu, Xiang Li, Pengye Zhang, Guozheng Li, Jie He, Han Li, and Kun Gai. 2018. Learning tree-based deep model for recommender systems. In KDD. ACM, 1079--1088.Google Scholar
- Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2020. Deep graph contrastive representation learning. arXiv preprint arXiv:2006.04131 (2020).Google Scholar
- Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021. 2069--2080.Google ScholarDigital Library
- Marinka Zitnik, Rok Sosivc, and Jure Leskovec. 2018. Prioritizing network communities. Nature communications, Vol. 9, 1 (2018), 1--9.Google Scholar
Index Terms
- Graph Contrastive Learning with Generative Adversarial Network
Recommendations
Self-supervised Graph-level Representation Learning with Adversarial Contrastive Learning
The recently developed unsupervised graph representation learning approaches apply contrastive learning into graph-structured data and achieve promising performance. However, these methods mainly focus on graph augmentation for positive samples, while the ...
ArieL: Adversarial Graph Contrastive Learning
Contrastive learning is an effective unsupervised method in graph representation learning. The key component of contrastive learning lies in the construction of positive and negative samples. Previous methods usually utilize the proximity of nodes in the ...
Adversarial Graph Contrastive Learning with Information Regularization
WWW '22: Proceedings of the ACM Web Conference 2022Contrastive learning is an effective unsupervised method in graph representation learning. Recently, the data augmentation based contrastive learning method has been extended from images to graphs. However, most prior works are directly adapted from the ...
Comments