{"type":"ScholarlyArticle","title":"Multi-view Contrastive Learning Hypergraph Neural Network for Drug-Microbe-Disease Association Prediction","authors":[{"name":"Luotao Liu","slug":"luotao-liu","given":"Luotao","family":"Liu","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Feng Huang","slug":"feng-huang","given":"Feng","family":"Huang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Xuan Liu","slug":"xuan-liu","given":"Xuan","family":"Liu","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Zhankun Xiong","slug":"zhankun-xiong","given":"Zhankun","family":"Xiong","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Menglu Li","slug":"menglu-li","given":"Menglu","family":"Li","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Congzhi Song","slug":"congzhi-song","given":"Congzhi","family":"Song","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"},{"name":"Wen Zhang","slug":"wen-zhang","given":"Wen","family":"Zhang","affiliation":"College of Informatics , Huazhong Agricultural University , Wuhan 430070 , China"}],"abstract":"Identifying the potential associations among drugs, microbes and diseases is of great significance in exploring the pathogenesis and improving precision medicine. There are plenty of computational methods for pair-wise association prediction, such as drug-microbe and microbe-disease associations, but few methods focus on the higher-order triple-wise drug-microbe-disease (DMD) associations. Driven by the advancement of hypergraph neural networks (HGNNs), we expect them to fully capture high-order interaction patterns behind the hypergraph formulated by DMD associations and realize sound prediction performance. However, the confirmed DMD associations are insufficient due to the high cost of in vitro screening, which forms a sparse DMD hypergraph and thus brings in suboptimal generalization ability. To mitigate the limitation, we propose a Multi-view Contrastive Learning Hypergraph Neural Network, named MCHNN, for DMD association prediction. We design a novel multi-view contrastive learning on the DMD hypergraph as an auxiliary task, which guides the HGNN to learn more discriminative representations and enhances the generalization ability. Extensive computational experiments show that MCHNN achieves satisfactory performance in DMD association prediction and, more importantly, demonstrate the effectiveness of our devised multi-view contrastive learning on the sparse DMD hypergraph.","doi":"10.24963/ijcai.2023/537","url":"https://ijcai.org/proceedings/2023/537","pdf":"https://ijcai.org/proceedings/2023/0537.pdf","markdown":"https://ijcai.org/proceedings/2023/537.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/537","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":537,"pages":{"first":4829,"last":4837},"keywords":["Multidisciplinary Topics and Applications: MDA: Health and medicine","Multidisciplinary Topics and Applications: MDA: Bioinformatics"],"videos":[],"references":[{"position":1,"raw":"[Andersen et al., 2020] Petter I Andersen, Aleksandr Ianevski, Hilde Lysvand, Astra Vitkauskiene, Valentyn Oksenych, Magnar Bjørås, Kaidi Telling, Irja Lutsar, Uga Dumpis, Yasuhiko Irie, et al. Discovery and devel- opment of safe-in-man broad-spectrum antiviral agents. International Journal of Infectious Diseases, 93:268-276, 2020.","title":"Discovery and development of safe-in-man broad-spectrum antiviral agents","authors":"Aleksandr Petter I Andersen, Hilde Ianevski, Astra Lysvand, Valentyn Vitkauskiene, Magnar Oksenych, Kaidi Bjørås, Irja Telling, Uga Lutsar, Yasuhiko Dumpis, Irie","year":"2020","venue":"International Journal of Infectious Diseases","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bai et al., 2021] Song Bai, Feihu Zhang, and Philip HS Torr. Hypergraph convolution and hypergraph attention. Pattern Recognition, 110:107637, 2021.","title":"Hypergraph convolution and hypergraph attention","authors":"Song Bai, Feihu Zhang, Philip Hs Torr","year":"2021","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Balažević et al., 2019] Ivana Balažević, Carl Allen, and Timothy M Hospedales. Tucker: Tensor factoriza- tion for knowledge graph completion. arXiv preprint arXiv:1901.09590, 2019.","title":"Tucker: Tensor factorization for knowledge graph completion","authors":"Ivana Balažević, Carl Allen, Timothy M Hospedales","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen and Li, 2019] Huiyuan Chen and Jing Li. Modeling relational drug-target-disease interactions via tensor fac- torization with multiple web sources. In The World Wide Web Conference, pages 218-227, 2019.","title":"Modeling relational drug-target-disease interactions via tensor factorization with multiple web sources","authors":"Chen, Li ; ] Huiyuan Chen, Jing Li","year":"2019","venue":"The World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen and Li, 2020] Huiyuan Chen and Jing Li. Learning data-driven drug-target-disease interaction via neural ten- sor network. In International Joint Conference on Artifi- cial Intelligence (IJCAI), 2020.","title":"Learning data-driven drug-target-disease interaction via neural tensor network","authors":"Chen, Li ; ] Huiyuan Chen, Jing Li","year":"2020","venue":"International Joint Conference on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2020/477"},{"position":6,"raw":"[Cheng et al., 2020] Liang Cheng, Changlu Qi, He Zhuang, Tongze Fu, and Xue Zhang. gutmdisorder: a com- prehensive database for dysbiosis of the gut microbiota in disorders and interventions. Nucleic Acids Research, 48(D1):D554-D560, 2020.","title":"gutmdisorder: a comprehensive database for dysbiosis of the gut microbiota in disorders and interventions","authors":"Liang Cheng, Changlu Qi, He Zhuang, Tongze Fu, Xue Zhang","year":"2020","venue":"Nucleic Acids Research","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Cheng et al., 2022] Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, and Xiangnan He. Ihgnn: Interactive hypergraph neural network for personalized product search. In Proceedings of the ACM Web Conference 2022, pages 256-265, 2022.","title":"Ihgnn: Interactive hypergraph neural network for personalized product search","authors":"Dian Cheng, Jiawei Chen, Wenjun Peng, Wenqin Ye, Fuyu Lv, Tao Zhuang, Xiaoyi Zeng, Xiangnan He","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Feng et al., 2019] Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. Hypergraph neural networks. In Proceedings of the AAAI Conference on Ar- tificial Intelligence, volume 33, pages 3558-3565, 2019.","title":"Hypergraph neural networks","authors":"Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, Yue Gao","year":"2019","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Gao et al., 2022] Yue Gao, Yifan Feng, Shuyi Ji, and Ron- grong Ji. Hgnnˆ+: General hypergraph neural networks. IEEE Transactions on Pattern Analysis and Machine In- telligence, 2022.","title":"Hgnnˆ+: General hypergraph neural networks","authors":"Yue Gao, Yifan Feng, Ji Shuyi, Rongrong Ji","year":"2022","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Guan et al., 2018] Saiping Guan, Xiaolong Jin, Yuanzhuo Wang, and Xueqi Cheng. Shared embedding based neural networks for knowledge graph completion. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pages 247-256, 2018.","title":"Shared embedding based neural networks for knowledge graph completion","authors":"Guan","year":"2018","venue":"Proceedings of the 27th ACM International Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Hassani and Khasahmadi, 2020] Kaveh Hassani and Amir Hosein Khasahmadi. Contrastive multi-view repre- sentation learning on graphs. In International Conference on Machine Learning, pages 4116-4126. PMLR, 2020.","title":"Contrastive multi-view representation learning on graphs","authors":"Kaveh Hassani, Amir Hosein, Khasahmadi","year":"2020","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Hjelm et al., 2018] R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Learning deep representa- tions by mutual information estimation and maximization. In International Conference on Learning Representations, 2018.","title":"Learning deep representations by mutual information estimation and maximization","authors":"Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, Yoshua Bengio","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Huang et al., 2018] Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, and Edward Y Chang. Improv- ing sequential recommendation with knowledge-enhanced memory networks. In The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 505-514, 2018.","title":"Improving sequential recommendation with knowledge-enhanced memory networks","authors":"Jin Huang, Wayne Xin Zhao, Hongjian Dou, Ji-Rong Wen, Edward Y Chang","year":"2018","venue":"The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2019] Jin Huang, Zhaochun Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, and Daxiang Dong. Taxonomy-aware multi-hop reasoning networks for se- quential recommendation. In Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pages 573-581, 2019.","title":"Taxonomy-aware multi-hop reasoning networks for sequential recommendation","authors":"Jin Huang, Zhaochun Ren, Wayne Xin Zhao, Gaole He, Ji-Rong Wen, Daxiang Dong","year":"2019","venue":"Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Janssens et al., 2018] Yorick Janssens, Joachim Nielandt, Antoon Bronselaer, Nathan Debunne, Frederick Verbeke, Evelien Wynendaele, Filip Van Immerseel, Yves-Paul Vandewynckel, Guy De Tré, and Bart De Spiegeleer. Dis- biome database: linking the microbiome to disease. BMC microbiology, 18(1):1-6, 2018.","title":"Disbiome database: linking the microbiome to disease","authors":"Yorick Janssens, Joachim Nielandt, Antoon Bronselaer, Nathan Debunne, Frederick Verbeke, Evelien Wynendaele, Filip Van Immerseel, Yves-Paul Vandewynckel, Guy De Tré, Bart De Spiegeleer","year":"2018","venue":"BMC microbiology","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ji et al., 2020] Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, and Yue Gao. Dual channel hyper- graph collaborative filtering. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 2020-2029, 2020.","title":"Dual channel hypergraph collaborative filtering","authors":"Shuyi Ji, Yifan Feng, Rongrong Ji, Xibin Zhao, Wanwan Tang, Yue Gao","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Jiang et al., 2019] Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, and Yue Gao. Dynamic hypergraph neural networks. In IJCAI, pages 2635-2641, 2019.","title":"Dynamic hypergraph neural networks","authors":"Jianwen Jiang, Yuxuan Wei, Yifan Feng, Jingxuan Cao, Yue Gao","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/366"},{"position":18,"raw":"[Kolda and Bader, 2009] Tamara G Kolda and Brett W Bader. Tensor decompositions and applications. SIAM review, 51(3):455-500, 2009.","title":"Tensor decompositions and applications","authors":"G Tamara, Brett W Kolda, Bader","year":"2009","venue":"SIAM review","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Li et al., 2022] Anchen Li, Bo Yang, Huan Huo, and Fa- rookh Hussain. Hypercomplex graph collaborative filter- ing. In Proceedings of the ACM Web Conference 2022, pages 1914-1922, 2022.","title":"Hypercomplex graph collaborative filtering","authors":"Anchen Li, Bo Yang, Huan Huo, Farookh Hussain","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Liu et al., 2019] Hanpeng Liu, Yaguang Li, Michael Tsang, and Yan Liu. Costco: A neural tensor completion model for sparse tensors. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discov- ery & Data Mining, pages 324-334, 2019.","title":"Costco: A neural tensor completion model for sparse tensors","authors":"Liu","year":"2019","venue":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Liu et al., 2021] Ruoqi Liu, Lai Wei, and Ping Zhang. A deep learning framework for drug repurposing via emulat- ing clinical trials on real-world patient data. Nature Ma- chine Intelligence, 3(1):68-75, 2021.","title":"A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data","authors":"Liu","year":"2021","venue":"Nature Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Liu et al., 2022] Xuan Liu, Congzhi Song, Shichao Liu, Menglu Li, Xionghui Zhou, and Wen Zhang. Multi- way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction. Bioinformatics, 38(20):4782-4789, 2022.","title":"Multiway relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction","authors":"Xuan Liu, Congzhi Song, Shichao Liu, Menglu Li, Xionghui Zhou, Wen Zhang","year":"2022","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Long et al., 2020a] Yahui Long, Min Wu, Yong Liu, Chee Keong Kwoh, Jiawei Luo, and Xiaoli Li. En- sembling graph attention networks for human microbe- drug association prediction. Bioinformatics, 36(Supple- ment 2):i779-i786, 2020.","title":"Ensembling graph attention networks for human microbedrug association prediction","authors":"Long","year":"2020","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Long et al., 2020b] Yahui Long, Min Wu, Yong Liu, Chee Keong Kwoh, Jiawei Luo, and Xiaoli Li. En- sembling graph attention networks for human microbe- drug association prediction. Bioinformatics, 36(Supple- ment 2):i779-i786, 2020.","title":"Ensembling graph attention networks for human microbedrug association prediction","authors":"Long","year":"2020","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Ma and Jiang, 2021] Yuanjing Ma and Hongmei Jiang. Nin- imhmda: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human microbe-disease association. Bioinformatics, 36(24):5665-5671, 2021.","title":"Ninimhmda: neural integration of neighborhood information on a multiplex heterogeneous network for multiple types of human microbe-disease association","authors":"Ma, Jiang, Yuanjing Ma, Hongmei Jiang","year":"2021","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Ma et al., 2017] Wei Ma, Lu Zhang, Pan Zeng, Chuanbo Huang, Jianwei Li, Bin Geng, Jichun Yang, Wei Kong, Xuezhong Zhou, and Qinghua Cui. An analysis of human microbe-disease associations. Briefings in bioinformatics, 18(1):85-97, 2017.","title":"An analysis of human microbe-disease associations","authors":"Wei Ma, Lu Zhang, Pan Zeng, Chuanbo Huang, Jianwei Li, Bin Geng, Jichun Yang, Wei Kong, Xuezhong Zhou, Qinghua Cui","year":"2017","venue":"Briefings in bioinformatics","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Qian et al., 2018] Wei Qian, Cong Fu, Yu Zhu, Deng Cai, and Xiaofei He. Translating embeddings for knowledge graph completion with relation attention mechanism. In IJCAI, pages 4286-4292, 2018.","title":"Translating embeddings for knowledge graph completion with relation attention mechanism","authors":"Wei Qian, Cong Fu, Yu Zhu, Deng Cai, Xiaofei He","year":"2018","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2018/596"},{"position":28,"raw":"[Rajput et al., 2018] Akanksha Rajput, Anamika Thakur, Shivangi Sharma, and Manoj Kumar. abiofilm: a re- source of anti-biofilm agents and their potential implica- tions in targeting antibiotic drug resistance. Nucleic acids research, 46(D1):D894-D900, 2018.","title":"abiofilm: a resource of anti-biofilm agents and their potential implications in targeting antibiotic drug resistance","authors":"Akanksha Rajput, Anamika Thakur, Shivangi Sharma, Manoj Kumar","year":"2018","venue":"Nucleic acids research","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sidorov et al., 2019] Pavel Sidorov, Stefan Naulaerts, Jérémy Ariey-Bonnet, Eddy Pasquier, and Pedro J Ballester. Predicting synergism of cancer drug combi- nations using nci-almanac data. Frontiers in chemistry, 7:509, 2019.","title":"Predicting synergism of cancer drug combinations using nci-almanac data","authors":"Sidorov","year":"2019","venue":"Frontiers in chemistry","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Skoufos et al., 2021] Giorgos Skoufos, Filippos S Kar- daras, Athanasios Alexiou, Ioannis Kavakiotis, Anastasia Lambropoulou, Vasiliki Kotsira, Spyros Tastsoglou, and Artemis G Hatzigeorgiou. Peryton: a manual collection of experimentally supported microbe-disease associations. Nucleic acids research, 49(D1):D1328-D1333, 2021.","title":"Peryton: a manual collection of experimentally supported microbe-disease associations","authors":"Giorgos Skoufos, S Filippos, Athanasios Kardaras, Ioannis Alexiou, Anastasia Kavakiotis, Vasiliki Lambropoulou, Spyros Kotsira, Artemis G Tastsoglou, Hatzigeorgiou","year":"2021","venue":"Nucleic acids research","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Sun et al., 2018] Ya-Zhou Sun, De-Hong Zhang, Shu-Bin Cai, Zhong Ming, Jian-Qiang Li, and Xing Chen. Mdad: a special resource for microbe-drug associations. Frontiers in cellular and infection microbiology, page 424, 2018.","title":"Mdad: a special resource for microbe-drug associations","authors":"Ya-Zhou Sun, De-Hong Zhang, Shu-Bin Cai, Zhong Ming, Jian-Qiang Li, Xing Chen","year":"2018","venue":"Frontiers in cellular and infection microbiology","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Van der Maaten and Hinton, 2008] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"Journal of machine learning research","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Velickovic et al., 2019] Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. ICLR (Poster), 2(3):4, 2019.","title":"Deep graph infomax","authors":"Petar Velickovic, William Fedus, Pietro William L Hamilton, Yoshua Liò, Devon Bengio, Hjelm","year":"2019","venue":"ICLR (Poster)","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wang et al., 2007] James Z Wang, Zhidian Du, Rapeeporn Payattakool, Philip S Yu, and Chin-Fu Chen. A new method to measure the semantic similarity of go terms. Bioinformatics, 23(10):1274-1281, 2007.","title":"A new method to measure the semantic similarity of go terms","authors":"Zhidian James Z Wang, Rapeeporn Du, Philip S Payattakool, Chin-Fu Yu, Chen","year":"2007","venue":"Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2021] Yueyue Wang, Xiujuan Lei, Cheng Lu, and Yi Pan. Predicting microbe-disease association based on multiple similarities and line algorithm. IEEE/ACM Transactions on Computational Biology and Bioinformat- ics, 2021.","title":"Predicting microbe-disease association based on multiple similarities and line algorithm","authors":"Yueyue Wang, Xiujuan Lei, Cheng Lu, Yi Pan","year":"2021","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2022a] Lei Wang, Yaqin Tan, Xiaoyu Yang, Linai Kuang, and Pengyao Ping. Review on predict- ing pairwise relationships between human microbes, drugs and diseases: from biological data to computational mod- els. Briefings in Bioinformatics, 2022.","title":"Review on predicting pairwise relationships between human microbes, drugs and diseases: from biological data to computational models","authors":"Wang","year":"2022","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Wang et al., 2022b] Xiaolin Wang, Guohao Sun, Xiu Fang, Jian Yang, and Shoujin Wang. Modeling spatio-temporal neighbourhood for personalized point-of-interest recom- mendation. In International Joint Conference on Artificial Intelligence (IJCAI), 2022.","title":"Modeling spatio-temporal neighbourhood for personalized point-of-interest recommendation","authors":"Wang","year":"2022","venue":"International Joint Conference on Artificial Intelligence (IJCAI)","doi":null,"url":null,"internal_url":"/proceedings/2022/490"},{"position":38,"raw":"[Wei et al., 2022] Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, and Zhangyang Wang. Augmen- tations in hypergraph contrastive learning: Fabricated and generative. arXiv preprint arXiv:2210.03801, 2022.","title":"Augmen-tations in hypergraph contrastive learning: Fabricated and generative","authors":"Tianxin Wei, Yuning You, Tianlong Chen, Yang Shen, Jingrui He, Zhangyang Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Welling and Kipf, 2016] Max Welling and Thomas N Kipf. Semi-supervised classification with graph convolutional networks. In J. International Conference on Learning Rep- resentations (ICLR 2017), 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"Kipf Welling, Max Welling, Thomas N Kipf","year":"2016","venue":"J. International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Wu et al., 2022] Shengbo Wu, Shujuan Yang, Manman Wang, Nan Song, Jie Feng, Hao Wu, Aidong Yang, Chun- jiang Liu, Yanni Li, Fei Guo, et al. Quorum sensing-based interactions among drugs, microbes, and diseases. Science China Life Sciences, pages 1-15, 2022.","title":"Quorum sensing-based interactions among drugs, microbes, and diseases","authors":"Shengbo Wu, Shujuan Yang, Manman Wang, Nan Song, Jie Feng, Hao Wu, Aidong Yang, Chunjiang Liu, Yanni Li, Fei Guo","year":"2022","venue":"Science China Life Sciences","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Xia et al., 2022a] Lianghao Xia, Chao Huang, Yong Xu, Ji- ashu Zhao, Dawei Yin, and Jimmy Huang. Hypergraph contrastive collaborative filtering. In Proceedings of the 45th International ACM SIGIR conference on research and development in information retrieval, pages 70-79, 2022.","title":"Hypergraph contrastive collaborative filtering","authors":"Xia","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":42,"raw":"[Xia et al., 2022b] Lianghao Xia, Chao Huang, and Chuxu Zhang. Self-supervised hypergraph transformer for rec- ommender systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 2100-2109, 2022.","title":"Self-supervised hypergraph transformer for recommender systems","authors":"Xia","year":"2022","venue":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Xu et al., 2019] Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. How powerful are graph neural net- works? In International Conference on Learning Rep- resentations, 2019.","title":"How powerful are graph neural networks","authors":"Xu","year":"2019","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Yadati et al., 2019] Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. Hypergcn: A new method for training graph convolutional networks on hypergraphs. Advances in neural information processing systems, 32, 2019.","title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs","authors":"Yadati","year":"2019","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Yang et al., 2022] Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S Yu, and Guandong Xu. Dual space graph contrastive learning. In Proceedings of the ACM Web Con- ference 2022, pages 1238-1247, 2022.","title":"Dual space graph contrastive learning","authors":"Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S Yu, Guandong Xu","year":"2022","venue":"Proceedings of the ACM Web Conference 2022","doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Yi and Park, 2020] Jaehyuk Yi and Jinkyoo Park. Hyper- graph convolutional recurrent neural network. In Proceed- ings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3366- 3376, 2020.","title":"Jaehyuk Yi and Jinkyoo Park. Hypergraph convolutional recurrent neural network","authors":"Park Yi","year":"2020","venue":"Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Yu et al., 2021] Junliang Yu, Hongzhi Yin, Jundong Li, Qinyong Wang, Nguyen Quoc Viet Hung, and Xiangliang Zhang. Self-supervised multi-channel hypergraph convo- lutional network for social recommendation. In Proceed- ings of the Web Conference 2021, pages 413-424, 2021.","title":"Self-supervised multi-channel hypergraph convolutional network for social recommendation","authors":"Yu","year":"2021","venue":"Proceedings of the Web Conference 2021","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Zhang et al., 2020] Ruochi Zhang, Yuesong Zou, and Jian Ma. Hyper-sagnn: a self-attention based graph neural network for hypergraphs. In International Conference on Learning Representations (ICLR), 2020.","title":"Hyper-sagnn: a self-attention based graph neural network for hypergraphs","authors":"Zhang","year":"2020","venue":"International Conference on Learning Representations (ICLR)","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}