{"type":"ScholarlyArticle","title":"Graph Contrastive Learning with Reinforcement Augmentation","authors":[{"name":"Ziyang Liu","slug":"ziyang-liu","given":"Ziyang","family":"Liu","affiliation":"School of Software , BNRist , Tsinghua University , Beijing , China"},{"name":"Chaokun Wang","slug":"chaokun-wang","given":"Chaokun","family":"Wang","affiliation":"School of Software , BNRist , Tsinghua University , Beijing , China"},{"name":"Cheng Wu","slug":"cheng-wu","given":"Cheng","family":"Wu","affiliation":"School of Software , BNRist , Tsinghua University , Beijing , China"}],"abstract":"Graph contrastive learning (GCL), designing contrastive objectives to learn embeddings from augmented graphs, has become a prevailing method for extracting embeddings from graphs in an unsupervised manner. As an important procedure in GCL, graph data augmentation (GDA) directly affects the model performance on downstream tasks. Currently, the GCL methods typically treat GDA as independent events, neglecting its continuity. In this paper, we regard the GDA in GCL as a Markov decision process and propose a novel graph reinforcement augmentation framework for GCL. Based on this framework, we design a Graph Advantage Actor-Critic (GA2C) model. We conduct extensive experiments to evaluate GA2C on unsupervised learning, transfer learning, and semi-supervised learning. The experimental results demonstrate the performance superiority of GA2C over the state-of-the-art GCL models. Furthermore, we verify that GA2C is more efficient than the other GCL methods with learnable GDA and provide two examples of chemical molecular graphs from ZINC-2M to demonstrate that GA2C generates meaningful augmented views, where the edge weights reflect the importance of chemical bonds in the molecule.","doi":"10.24963/ijcai.2024/246","url":"https://ijcai.org/proceedings/2024/246","pdf":"https://ijcai.org/proceedings/2024/0246.pdf","markdown":"https://ijcai.org/proceedings/2024/246.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/246","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":246,"pages":{"first":2225,"last":2233},"keywords":["Data Mining: DM: Mining graphs","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Representation learning","Machine Learning: ML: Reinforcement learning"],"videos":[],"references":[{"position":1,"raw":"[Adhikari et al., 2018] Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, and B. Aditya Prakash. Sub2vec: Feature learning for subgraphs. In Advances in Knowledge Discov- ery and Data Mining: 22nd Pacific-Asia Conference, pages 170-182, Melbourne, VIC, Australia, June 2018. Springer.","title":"Sub2vec: Feature learning for subgraphs","authors":"Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, B Aditya Prakash","year":"2018","venue":"Advances in Knowledge Discovery and Data Mining: 22nd Pacific-Asia Conference","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ang et al., 2021] Andersen Man Shun Ang, Jianzhu Ma, Ni- anjun Liu, Kun Huang, and Yijie Wang. Fast projection onto the capped simplex with applications to sparse regres- sion in bioinformatics. In Thirty-fifth Annual Conference on Neural Information Processing Systems, pages 9990-9999, Virtual Event, December 2021. MIT Press.","title":"Fast projection onto the capped simplex with applications to sparse regression in bioinformatics","authors":"Andersen Man, Shun Ang, Jianzhu Ma, Nianjun Liu, Kun Huang, Yijie Wang","year":"2021","venue":"Thirty-fifth Annual Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 2020] Emmanuel Bengio, Joelle Pineau, and Doina Precup. Interference and generalization in temporal difference learning. In Proceedings of the 37th Interna- tional Conference on Machine Learning, pages 767-777, Virtual Event, July 2020. PMLR.","title":"Interference and generalization in temporal difference learning","authors":"Emmanuel Bengio, Joelle Pineau, Doina Precup","year":"2020","venue":"Proceedings of the 37th International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2019] Ting Chen, Song Bian, and Yizhou Sun. Are powerful graph neural nets necessary? a dissection on graph classification. CoRR, abs/1905.04579, 2019.","title":"Are powerful graph neural nets necessary? a dissection on graph classification","authors":"Ting Chen, Song Bian, Yizhou Sun","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Cui et al., 2019] Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. A survey on network embedding. IEEE Trans. Knowl. Data Eng., 31(5):833-852, 2019.","title":"A survey on network embedding","authors":"Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu","year":"2019","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864, San Francisco, CA, USA, August 2016. ACM.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hu et al., 2020a] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural informa- tion processing systems, 33:22118-22133, 2020.","title":"Open graph benchmark: Datasets for machine learning on graphs","authors":"Hu","year":"2020","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hu et al., 2020b] Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay S. Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. In 8th International Conference on Learning Representa- tions, Addis Ababa, Ethiopia, April 2020. OpenReview.net.","title":"Strategies for pre-training graph neural networks","authors":"Hu","year":"2020","venue":"8th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ju et al., 2023] Mingxuan Ju, Yujie Fan, Chuxu Zhang, and Yanfang Ye. Let graph be the go board: gradient-free node injection attack for graph neural networks via reinforcement learning. In Thirty-Seventh AAAI Conference on Artificial Intelligence, pages 4383-4390, Washington, DC, USA, February 2023. AAAI Press.","title":"Let graph be the go board: gradient-free node injection attack for graph neural networks via reinforcement learning","authors":"Ju","year":"2023","venue":"Thirty-Seventh AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Liu et al., 2022] Ziyang Liu, Chaokun Wang, Hao Feng, Lingfei Wu, and Liqun Yang. Knowledge distillation based contextual relevance matching for e-commerce product search. In Proceedings of the 2022 Conference on Empir- ical Methods in Natural Language Processing -Industry Track, Abu Dhabi, UAE, December 2022. Association for Computational Linguistics.","title":"Knowledge distillation based contextual relevance matching for e-commerce product search","authors":"Liu","year":"2022","venue":"Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing -Industry Track","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Liu et al., 2023] Ziyang Liu, Chaokun Wang, Yunkai Lou, and Hao Feng. Fast unsupervised graph embedding via graph zoom learning. In 39th IEEE International Confer- ence on Data Engineering, pages 2551-2564, Anaheim, CA, USA, April 2023. IEEE.","title":"Fast unsupervised graph embedding via graph zoom learning","authors":"Liu","year":"2023","venue":"39th IEEE International Conference on Data Engineering","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Morris et al., 2020] Christopher Morris, Nils M. Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann. Tudataset: A collection of benchmark datasets for learning with graphs. In ICML Workshop on Graph Representation Learning and Beyond, 2020.","title":"Tudataset: A collection of benchmark datasets for learning with graphs","authors":"Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, Marion Neumann","year":"2020","venue":"ICML Workshop on Graph Representation Learning and Beyond","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Munikoti et al., 2023] Sai Munikoti, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, and Balasubramaniam Natarajan. Challenges and opportunities in deep reinforce- ment learning with graph neural networks: A comprehen- sive review of algorithms and applications. IEEE Trans- actions on Neural Networks and Learning Systems, pages 1-21, 2023.","title":"Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications","authors":"Sai Munikoti, Deepesh Agarwal, Laya Das, Mahantesh Halappanavar, Balasubramaniam Natarajan","year":"2023","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Narayanan et al., 2017] Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, and Shantanu Jaiswal. graph2vec: Learning distributed representations of graphs. In Proceedings of the 13th Inter- national Workshop on Mining and Learning with Graphs (MLG), 2017.","title":"graph2vec: Learning distributed representations of graphs","authors":"Annamalai Narayanan, Mahinthan Chandramohan, Rajasekar Venkatesan, Lihui Chen, Yang Liu, Shantanu Jaiswal","year":"2017","venue":"Proceedings of the 13th International Workshop on Mining and Learning with Graphs (MLG)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Shervashidze et al., 2009] Nino Shervashidze, S. V. N. Vish- wanathan, Tobias Petri, Kurt Mehlhorn, and Karsten M. Borgwardt. Efficient graphlet kernels for large graph comparison. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, pages 488-495, Clearwater Beach, Florida, USA, April 2009. JMLR.org.","title":"Efficient graphlet kernels for large graph comparison","authors":"S V N Nino Shervashidze, Tobias Vishwanathan, Kurt Petri, Karsten M Mehlhorn, Borgwardt","year":"2009","venue":"Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shervashidze et al., 2011] Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M. Borgwardt. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, 12:2539-2561, 2011.","title":"Weisfeiler-lehman graph kernels","authors":"Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, Karsten M Borgwardt","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Sun et al., 2020] Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang. Infograph: Unsupervised and semi- supervised graph-level representation learning via mutual information maximization. In 8th International Conference on Learning Representations, Addis Ababa, Ethiopia, April 2020. OpenReview.net.","title":"Infograph: Unsupervised and semisupervised graph-level representation learning via mutual information maximization","authors":"Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, Jian Tang","year":"2020","venue":"8th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Suresh et al., 2021] Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. Adversarial graph augmentation to im- prove graph contrastive learning. In Thirty-seventh Annual Conference on Neural Information Processing Systems, Vir- tual Event, December 2021. MIT Press.","title":"Adversarial graph augmentation to improve graph contrastive learning","authors":"Susheel Suresh, Pan Li, Cong Hao, Jennifer Neville","year":"2021","venue":"Thirty-seventh Annual Conference on Neural Information Processing Systems, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Thakoor et al., 2021] Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velick- ovic, and Michal Valko. Bootstrapped representation learn- ing on graphs. ICLR Workshop on Geometrical and Topo- logical Representation Learning, 2021.","title":"Bootstrapped representation learning on graphs","authors":"Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Rémi Munos, Petar Velickovic, Michal Valko","year":"2021","venue":"ICLR Workshop on Geometrical and Topological Representation Learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tschannen et al., 2020] Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic. On mutual information maximization for representation learn- ing. In 8th International Conference on Learning Repre- sentations, Addis Ababa, Ethiopia, April 2020. OpenRe- view.net.","title":"On mutual information maximization for representation learning","authors":"Josip Michael Tschannen, Paul K Djolonga, Sylvain Rubenstein, Mario Gelly, Lucic","year":"2020","venue":"8th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Velickovic et al., 2019] Petar Velickovic, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. De- von Hjelm. Deep graph infomax. In 7th International Con- ference on Learning Representations, New Orleans, LA, USA, May 2019. OpenReview.net.","title":"Deep graph infomax","authors":"Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, R, Devon Hjelm","year":"2019","venue":"7th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2018] Zhen Wang, Marko Jusup, Lei Shi, Joung- Hun Lee, Yoh Iwasa, and Stefano Boccaletti. Exploiting a cognitive bias promotes cooperation in social dilemma experiments. Nature communications, 9(1):2954, 2018.","title":"Exploiting a cognitive bias promotes cooperation in social dilemma experiments","authors":"Zhen Wang, Marko Jusup, Lei Shi, Joung-Hun Lee, Yoh Iwasa, Stefano Boccaletti","year":"2018","venue":"Nature communications","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2022a] Haonan Wang, Jieyu Zhang, Qi Zhu, and Wei Huang. Augmentation-free graph contrastive learn- ing. arXiv preprint arXiv:2204.04874, 2022.","title":"Augmentation-free graph contrastive learning","authors":"Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2022b] Zhen Wang, Chunjiang Mu, Shuyue Hu, Chen Chu, and Xuelong Li. Modelling the dynamics of regret minimization in large agent populations: a master equation approach. In Proceedings of the Thirty-First Inter- national Joint Conference on Artificial Intelligence, pages 534-540, Vienna, Austria, July 2022.","title":"Modelling the dynamics of regret minimization in large agent populations: a master equation approach","authors":"Wang","year":"2022","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2022/76"},{"position":25,"raw":"[Morgan Kaufmann. Wu et al., 2023] Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song, and Kun Gai. Graph contrastive learning with generative adver- sarial network. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2721-2730, Long Beach, CA, USA, August 2023. ACM.","title":"Graph contrastive learning with generative adversarial network","authors":"Cheng Wu, Chaokun Wang, Jingcao Xu, Ziyang Liu, Kai Zheng, Xiaowei Wang, Yang Song, Kun Gai","year":"2023","venue":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xia et al., 2022] Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z. Li. Simgrace: A simple framework for graph contrastive learning without data augmentation. In Proceedings of the Web Conference 2022, pages 1070-1079, Lyon, France, April 2022. ACM.","title":"Simgrace: A simple framework for graph contrastive learning without data augmentation","authors":"Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, Stan Z Li","year":"2022","venue":"Proceedings of the Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Xu et al., 2019] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural networks? In 7th International Conference on Learning Representa- tions, New Orleans, LA, USA, May 2019. OpenReview.net.","title":"How powerful are graph neural networks?","authors":"Xu","year":"2019","venue":"7th International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Xu et al., 2023] Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, and Kun Gai. Multi-behavior self-supervised learning for recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and De- velopment in Information Retrieval, pages 496-505, Taipei, Taiwan, July 2023. ACM.","title":"Multi-behavior self-supervised learning for recommendation","authors":"Jingcao Xu, Chaokun Wang, Cheng Wu, Yang Song, Kai Zheng, Xiaowei Wang, Changping Wang, Guorui Zhou, Kun Gai","year":"2023","venue":"Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Yanardag and Vishwanathan, 2015] Pinar Yanardag and S. V. N. Vishwanathan. Deep graph kernels. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1365-1374, Sydney, NSW, Australia, August 2015. Springer.","title":"Deep graph kernels","authors":"Vishwanathan ; ] Pinar Yanardag, S V N Yanardag, Vishwanathan","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yang et al., 2022] Liang Yang, Wenmiao Zhou, Weihang Peng, Bingxin Niu, Junhua Gu, Chuan Wang, Xiaochun Cao, and Dongxiao He. Graph neural networks beyond compromise between attribute and topology. In Proceed- ings of the Web Conference 2022, pages 1127-1135, Lyon, France, April 2022. ACM.","title":"Graph neural networks beyond compromise between attribute and topology","authors":"Liang Yang, Wenmiao Zhou, Weihang Peng, Bingxin Niu, Junhua Gu, Chuan Wang, Xiaochun Cao, Dongxiao He","year":"2022","venue":"Proceedings of the Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Yang et al., 2023] Liang Yang, Qiuliang Zhang, Runjie Shi, Wenmiao Zhou, Bingxin Niu, Chuan Wang, Xiaochun Cao, Dongxiao He, Zhen Wang, and Yuanfang Guo. Graph neural networks without propagation. In Proceedings of the Web Conference 2023, pages 469-477, Austin, TX, USA, April 2023. ACM.","title":"Graph neural networks without propagation","authors":"Liang Yang, Qiuliang Zhang, Runjie Shi, Wenmiao Zhou, Bingxin Niu, Chuan Wang, Xiaochun Cao, Dongxiao He, Zhen Wang, Yuanfang Guo","year":"2023","venue":"Proceedings of the Web Conference 2023","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Yin et al., 2022] Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, and Xiang Zhang. Autogcl: Automated graph contrastive learning via learnable view generators. In Thirty-Sixth AAAI Conference on Artificial Intelligence, pages 8892-8900, Virtual Event, February 2022. AAAI Press.","title":"Autogcl: Automated graph contrastive learning via learnable view generators","authors":"Yihang Yin, Qingzhong Wang, Siyu Huang, Haoyi Xiong, Xiang Zhang","year":"2022","venue":"Thirty-Sixth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[You et al., 2020] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph con- trastive learning with augmentations. In Thirty-sixth Annual Conference on Neural Information Processing Systems, Vir- tual Event, December 2020. MIT Press.","title":"Graph contrastive learning with augmentations","authors":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen","year":"2020","venue":"Thirty-sixth Annual Conference on Neural Information Processing Systems, Virtual Event","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[You et al., 2021] Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. Graph contrastive learning auto- mated. In International Conference on Machine Learning, pages 12121-12132, Virtual Event, July 2021. PMLR.","title":"Graph contrastive learning automated","authors":"You","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[You et al., 2022] Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. Bringing your own view: Graph contrastive learning without prefabricated data augmenta- tions. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pages 1300- 1309, Tempe, AZ, USA, February 2022. ACM.","title":"Bringing your own view: Graph contrastive learning without prefabricated data augmentations","authors":"You","year":"2022","venue":"Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yu et al., 2022] Junliang Yu, Hongzhi Yin, Xin Xia, Tong Chen, Lizhen Cui, and Quoc Viet Hung Nguyen. Are graph augmentations necessary? simple graph contrastive learning for recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1294-1303, Madrid, Spain, July 2022. ACM.","title":"Are graph augmentations necessary? simple graph contrastive learning for recommendation","authors":"Yu","year":"2022","venue":"Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhu et al., 2020] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Deep graph contrastive representation learning. In ICML Workshop on Graph Rep- resentation Learning and Beyond, 2020.","title":"Deep graph contrastive representation learning","authors":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang","year":"2020","venue":"ICML Workshop on Graph Representation Learning and Beyond","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhu et al., 2021] Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021, pages 2069-2080, Ljubljana, Slovenia, April 2021. ACM.","title":"Graph contrastive learning with adaptive augmentation","authors":"Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, Liang Wang","year":"2021","venue":"Proceedings of the Web Conference 2021","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}