{"type":"ScholarlyArticle","title":"Curriculum Graph Machine Learning: A Survey","authors":[{"name":"Haoyang Li","slug":"haoyang-li","given":"Haoyang","family":"Li","affiliation":"Department of Computer Science and Technology , Tsinghua University"},{"name":"Xin Wang","slug":"xin-wang","given":"Xin","family":"Wang","affiliation":"Department of Computer Science and Technology , Tsinghua University; Beijing National Research Center for Information Science and Technology , Tsinghua University"},{"name":"Wenwu Zhu","slug":"wenwu-zhu","given":"Wenwu","family":"Zhu","affiliation":"Department of Computer Science and Technology , Tsinghua University; Beijing National Research Center for Information Science and Technology , Tsinghua University"}],"abstract":"Graph machine learning has been extensively studied in both academia and industry. However, in the literature, most existing graph machine learning models are designed to conduct training with data samples in a random order, which may suffer from suboptimal performance due to ignoring the importance of different graph data samples and their training orders for the model optimization status. To tackle this critical problem, curriculum graph machine learning (Graph CL), which integrates the strength of graph machine learning and curriculum learning, arises and attracts an increasing amount of attention from the research community. Therefore, in this paper, we comprehensively overview approaches on Graph CL and present a detailed survey of recent advances in this direction. Specifically, we first discuss the key challenges of Graph CL and provide its formal problem definition. Then, we categorize and summarize existing methods into three classes based on three kinds of graph machine learning tasks, i.e., node-level, link-level, and graph-level tasks. Finally, we share our thoughts on future research directions. To the best of our knowledge, this paper is the first survey for curriculum graph machine learning.","doi":"10.24963/ijcai.2023/748","url":"https://ijcai.org/proceedings/2023/748","pdf":"https://ijcai.org/proceedings/2023/0748.pdf","markdown":"https://ijcai.org/proceedings/2023/748.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/748","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":"Survey Track","track":"Survey Track","number":748,"pages":{"first":6674,"last":6682},"keywords":["Survey: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2022] Usman Ahmed, Jerry Chun-Wei Lin, and Gautam Srivastava. Hyper-graph-based attention curriculum learning using a lexical algorithm for mental health. Pattern Recognition Letters, 2022.","title":"Hyper-graph-based attention curriculum learning using a lexical algorithm for mental health","authors":"Usman Ahmed, Jerry Chun-Wei Lin, Gautam Srivastava","year":"2022","venue":"Pattern Recognition Letters","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Applegate et al., 2011] David L Applegate, Robert E Bixby, Vašek Chvátal, and William J Cook. The traveling salesman problem. In The Traveling Salesman Problem. 2011.","title":"The traveling salesman problem","authors":"Robert E David L Applegate, Vašek Bixby, William J Chvátal, Cook","year":"2011","venue":"The Traveling Salesman Problem","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 2009] Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. Curriculum learning. In ICML, 2009.","title":"Curriculum learning","authors":"Yoshua Bengio, Jérôme Louradour, Ronan Collobert, Jason Weston","year":"2009","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Box et al., 2015] George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung. Time series analysis: forecasting and control. 2015.","title":"Time series analysis: forecasting and control","authors":"George Ep Box, M Gwilym, Gregory C Jenkins, Greta M Reinsel, Ljung","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Castells et al., 2020] Thibault Castells, Philippe Weinzaepfel, and Jerome Revaud. Superloss: A generic loss for robust curriculum learning. NeurIPS, 2020.","title":"Superloss: A generic loss for robust curriculum learning","authors":"Thibault Castells, Philippe Weinzaepfel, Jerome Revaud","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chu et al., 2021] Guanyi Chu, Xiao Wang, Chuan Shi, and Xun- qiang Jiang. Cuco: Graph representation with curriculum con- trastive learning. In IJCAI, 2021.","title":"Cuco: Graph representation with curriculum contrastive learning","authors":"Guanyi Chu, Xiao Wang, Chuan Shi, Xunqiang Jiang","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/317"},{"position":7,"raw":"[Cui et al., 2018] Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. A survey on network embedding. IEEE TKDE, 2018.","title":"A survey on network embedding","authors":"Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu","year":"2018","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[de Vico Fallani et al., 2014] Fabrizio de Vico Fallani, Jonas Richiardi, Mario Chavez, and Sophie Achard. Graph analysis of functional brain networks: practical issues in translational neu- roscience. Philosophical Transactions of the Royal Society B: Biological Sciences, 2014.","title":"Graph analysis of functional brain networks: practical issues in translational neuroscience","authors":"Fallani De Vico","year":"2014","venue":"Philosophical Transactions of the Royal Society B: Biological Sciences","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dong et al., 2021] Xinzhi Dong, Chengjiang Long, Wenju Xu, and Chunxia Xiao. Dual graph convolutional networks with trans- former and curriculum learning for image captioning. In ACM MM, 2021.","title":"Dual graph convolutional networks with transformer and curriculum learning for image captioning","authors":"Dong","year":"2021","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Elman, 1993] Jeffrey L Elman. Learning and development in neu- ral networks: The importance of starting small. Cognition, 1993.","title":"Learning and development in neural networks: The importance of starting small","authors":"Jeffrey L Elman","year":"1993","venue":"Cognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Gao et al., 2017] Yue Gao, Hanwang Zhang, Xibin Zhao, and Shuicheng Yan. Event classification in microblogs via social tracking. ACM TIST, 2017.","title":"Event classification in microblogs via social tracking","authors":"Yue Gao, Hanwang Zhang, Xibin Zhao, Shuicheng Yan","year":"2017","venue":"ACM TIST","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gasteiger et al., 2018] Johannes Gasteiger, Aleksandar Bo- jchevski, and Stephan Günnemann. Predict then propa- gate: Graph neural networks meet personalized pagerank. arXiv:1810.05997, 2018.","title":"Predict then propagate: Graph neural networks meet personalized pagerank","authors":"Gasteiger","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gilmer et al., 2017] Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. Neural message passing for quantum chemistry. In ICML, 2017.","title":"Neural message passing for quantum chemistry","authors":"Justin Gilmer, S Samuel, Patrick F Schoenholz, Oriol Riley, George E Vinyals, Dahl","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Gong et al., 2016] Tieliang Gong, Qian Zhao, Deyu Meng, and Zongben Xu. Why curriculum learning & self-paced learning work in big/noisy data: A theoretical perspective. Big Data and Information Analytics, 2016.","title":"Why curriculum learning & self-paced learning work in big/noisy data: A theoretical perspective","authors":"Gong","year":"2016","venue":"Big Data and Information Analytics","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Gong et al., 2019] Chen Gong, Jian Yang, and Dacheng Tao. Multi-modal curriculum learning over graphs. ACM TIST, 2019.","title":"Multi-modal curriculum learning over graphs","authors":"Chen Gong, Jian Yang, Dacheng Tao","year":"2019","venue":"ACM TIST","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Gu et al., 2022] Yaowen Gu, Si Zheng, Zidu Xu, Qijin Yin, Liang Li, and Jiao Li. An efficient curriculum learning-based strategy for molecular graph learning. Briefings in Bioinformatics, 2022.","title":"An efficient curriculum learning-based strategy for molecular graph learning","authors":"Yaowen Gu, Si Zheng, Zidu Xu, Qijin Yin, Liang Li, Jiao Li","year":"2022","venue":"Briefings in Bioinformatics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Guan et al., 2022] Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, and Wenwu Zhu. Large-scale graph neural architecture search. In ICML, 2022.","title":"Large-scale graph neural architecture search","authors":"Guan","year":"2022","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Guo et al., 2018] Sheng Guo, Weilin Huang, Haozhi Zhang, Chen- fan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang. Curriculumnet: Weakly supervised learning from large- scale web images. In ECCV, 2018.","title":"Curriculumnet: Weakly supervised learning from largescale web images","authors":"Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, Dinglong Huang","year":"2018","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Hacohen and Weinshall, 2019] Guy Hacohen and Daphna Wein- shall. On the power of curriculum learning in training deep net- works. In ICML, 2019.","title":"On the power of curriculum learning in training deep networks","authors":"Weinshall Hacohen","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. NeurIPS, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Hochba, 1997] Dorit S Hochba. Approximation algorithms for np- hard problems. ACM Sigact News, 1997.","title":"Approximation algorithms for nphard problems","authors":"Dorit S Hochba","year":"1997","venue":"ACM Sigact News","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Horry et al., 2020] Michael J Horry, Subrata Chakraborty, Manoranjan Paul, Anwaar Ulhaq, Biswajeet Pradhan, Manas Saha, and Nagesh Shukla. Covid-19 detection through transfer learning using multimodal imaging data. Ieee Access, 2020.","title":"Covid-19 detection through transfer learning using multimodal imaging data","authors":"J Michael, Subrata Horry, Manoranjan Chakraborty, Anwaar Paul, Biswajeet Ulhaq, Manas Pradhan, Nagesh Saha, Shukla","year":"2020","venue":"Ieee Access","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Hu et al., 2020a] Weihua Hu, Matthias Fey, Marinka Zitnik, Yux- iao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learn- ing on graphs. NeurIPS, 2020.","title":"Open graph benchmark: Datasets for machine learning on graphs","authors":"Hu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Hu et al., 2020b] Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. In ICLR, 2020.","title":"Strategies for pre-training graph neural networks","authors":"Hu","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Hu et al., 2022] Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, and Jure Leskovec. Tuneup: A training strategy for improving generalization of graph neural networks. arXiv:2210.14843, 2022.","title":"Tuneup: A training strategy for improving generalization of graph neural networks","authors":"Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, Jure Leskovec","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Iscen et al., 2019] Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum. Label propagation for deep semi-supervised learning. In CVPR, 2019.","title":"Label propagation for deep semi-supervised learning","authors":"Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, Ondrej Chum","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Jiang et al., 2014] Lu Jiang, Deyu Meng, Teruko Mitamura, and Alexander G Hauptmann. Easy samples first: Self-paced rerank- ing for zero-example multimedia search. In ACM MM, 2014.","title":"Easy samples first: Self-paced reranking for zero-example multimedia search","authors":"Lu Jiang, Deyu Meng, Teruko Mitamura, Alexander G Hauptmann","year":"2014","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Kipf and Welling, 2017] Thomas N Kipf and Max Welling. Semi- supervised classification with graph convolutional networks. In ICLR, 2017.","title":"Semisupervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Li et al., 2022a] Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. Ood-gnn: Out-of-distribution generalized graph neural net- work. IEEE TKDE, 2022.","title":"Ood-gnn: Out-of-distribution generalized graph neural network","authors":"Li","year":"2022","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Li et al., 2022b] Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. Out-of-distribution generalization on graphs: A sur- vey. arXiv:2202.07987, 2022.","title":"Out-of-distribution generalization on graphs: A survey","authors":"Li","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Li et al., 2022c] Xiaohe Li, Lijie Wen, Yawen Deng, Fuli Feng, Xuming Hu, Lei Wang, and Zide Fan. Graph neural net- work with curriculum learning for imbalanced node classifica- tion. arXiv:2202.02529, 2022.","title":"Graph neural network with curriculum learning for imbalanced node classification","authors":"Li","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Liu et al., 2023a] Hanwen Liu, Peilin Zhao, Tingyang Xu, Yatao Bian, Junzhou Huang, Yuesheng Zhu, and Yadong Mu. Curricu- lum graph poisoning. In The WebConf, 2023.","title":"Curriculum graph poisoning","authors":"Liu","year":"2023","venue":"The WebConf","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Liu et al., 2023b] Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, and Cancan Chen. Hard sample aware network for con- trastive deep graph clustering. AAAI, 2023.","title":"Hard sample aware network for contrastive deep graph clustering","authors":"Liu","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Pham et al., 2018] Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via param- eters sharing. In ICML, 2018.","title":"Efficient neural architecture search via parameters sharing","authors":"Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, Jeff Dean","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Platanios et al., 2019] Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom M Mitchell. Competence-based curriculum learning for neural machine translation. arXiv:1903.09848, 2019.","title":"Competence-based curriculum learning for neural machine translation","authors":"Otilia Emmanouil Antonios Platanios, Graham Stretcu, Barnabas Neubig, Tom M Poczos, Mitchell","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Puterman, 1990] Martin L Puterman. Markov decision processes. Handbooks in operations research and management science, 1990.","title":"Markov decision processes","authors":"Martin L Puterman","year":"1990","venue":"Handbooks in operations research and management science","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Qu et al., 2018] Meng Qu, Jian Tang, and Jiawei Han. Curricu- lum learning for heterogeneous star network embedding via deep reinforcement learning. In WSDM, 2018.","title":"Curriculum learning for heterogeneous star network embedding via deep reinforcement learning","authors":"Meng Qu, Jian Tang, Jiawei Han","year":"2018","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Rohde and Plaut, 1999] Douglas LT Rohde and David C Plaut. Language acquisition in the absence of explicit negative evi- dence: How important is starting small? Cognition, 1999.","title":"Language acquisition in the absence of explicit negative evidence: How important is starting small? Cognition","authors":"Plaut ; Rohde, L T Douglas, David C Rohde, Plaut","year":"1999","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Roy et al., 2021] Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, and Elisa Ricci. Curriculum graph co- teaching for multi-target domain adaptation. In CVPR, 2021.","title":"Curriculum graph coteaching for multi-target domain adaptation","authors":"Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, Elisa Ricci","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Sanchez-Gonzalez et al., 2020] Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter Battaglia. Learning to simulate complex physics with graph networks. In ICML, 2020.","title":"Learning to simulate complex physics with graph networks","authors":"Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, Peter Battaglia","year":"2020","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Soviany et al., 2022] Petru Soviany, Radu Tudor Ionescu, Paolo Rota, and Nicu Sebe. Curriculum learning: A survey. IJCV, 2022.","title":"Curriculum learning: A survey","authors":"Petru Soviany, Tudor Radu, Paolo Ionescu, Nicu Rota, Sebe","year":"2022","venue":"IJCV","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Sun et al., 2009] Yizhou Sun, Yintao Yu, and Jiawei Han. Ranking-based clustering of heterogeneous information net- works with star network schema. In SIGKDD, 2009.","title":"Ranking-based clustering of heterogeneous information networks with star network schema","authors":"Yizhou Sun, Yintao Yu, Jiawei Han","year":"2009","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Sun et al., 2011] Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. VLDB Endow- ment, 2011.","title":"Pathsim: Meta path-based top-k similarity search in heterogeneous information networks","authors":"Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, Tianyi Wu","year":"2011","venue":"VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Sun et al., 2019] Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang. Infograph: Unsupervised and semi-supervised graph- level representation learning via mutual information maximiza- tion. In ICLR, 2019.","title":"Infograph: Unsupervised and semi-supervised graphlevel representation learning via mutual information maximization","authors":"Fan-Yun Sun, Jordan Hoffman, Vikas Verma, Jian Tang","year":"2019","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Tay et al., 2019] Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, and Aston Zhang. Simple and effective curriculum pointer- generator networks for reading comprehension over long narra- tives. arXiv:1905.10847, 2019.","title":"Simple and effective curriculum pointergenerator networks for reading comprehension over long narratives","authors":"Yi Tay, Shuohang Wang, Anh Luu, Jie Tuan, Minh C Fu, Xingdi Phan, Jinfeng Yuan, Siu Cheung Rao, Aston Hui, Zhang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Tokdar and Kass, 2010] Surya T Tokdar and Robert E Kass. Im- portance sampling: a review. Wiley Interdisciplinary Reviews: Computational Statistics, 2010.","title":"Importance sampling: a review","authors":"Kass ; Tokdar, T Surya, Robert E Tokdar, Kass","year":"2010","venue":"Wiley Interdisciplinary Reviews: Computational Statistics","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Tong et al., 2020] Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim. Digraph incep- tion convolutional networks. NeurIPS, 2020.","title":"Digraph inception convolutional networks","authors":"Tong","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Tong et al., 2021] Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang. Directed graph contrastive learning. NeurIPS, 2021.","title":"Directed graph contrastive learning","authors":"Tong","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Vakil and Amiri, 2022] Nidhi Vakil and Hadi Amiri. Generic and trend-aware curriculum learning for relation extraction in graph neural networks. arXiv:2205.08625, 2022.","title":"Generic and trend-aware curriculum learning for relation extraction in graph neural networks","authors":"Amiri ; ] Nidhi Vakil, Hadi Vakil, Amiri","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Veličković et al., 2018] Petar Veličković, Guillem Cucurull, Aran- txa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. Graph attention networks. In ICLR, 2018.","title":"Graph attention networks","authors":"Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, Yoshua Bengio","year":"2018","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Wang et al., 2017] Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. Knowledge graph embedding: A survey of approaches and applications. IEEE TKDE, 2017.","title":"Knowledge graph embedding: A survey of approaches and applications","authors":"Quan Wang, Zhendong Mao, Bin Wang, Li Guo","year":"2017","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Wang et al., 2019] Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, and Junjie Yan. Dynamic curriculum learning for imbalanced data classification. In ICCV, 2019.","title":"Dynamic curriculum learning for imbalanced data classification","authors":"Wang","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Wang et al., 2021a] Xin Wang, Yudong Chen, and Wenwu Zhu. A survey on curriculum learning. IEEE TPAMI, 2021.","title":"A survey on curriculum learning","authors":"Wang","year":"2021","venue":"IEEE TPAMI","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Wang et al., 2021b] Yiwei Wang, Wei Wang, Yuxuan Liang, Yujun Cai, and Bryan Hooi. Curgraph: Curriculum learning for graph classification. In The WebConf, 2021.","title":"Curgraph: Curriculum learning for graph classification","authors":"Wang","year":"2021","venue":"The WebConf","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Wang et al., 2023] Hui Wang, Kun Zhou, Xin Zhao, Jingyuan Wang, and Ji-Rong Wen. Curriculum pre-training heterogeneous subgraph transformer for top-n recommendation. TOIS, 2023.","title":"Curriculum pre-training heterogeneous subgraph transformer for top-n recommendation","authors":"Hui Wang, Kun Zhou, Xin Zhao, Jingyuan Wang, Ji-Rong Wen","year":"2023","venue":"TOIS","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Wei et al., 2022] Xiaowen Wei, Weiwei Liu, Yibing Zhan, Du Bo, and Wenbin Hu. Clnode: Curriculum learning for node classifi- cation. arXiv:2206.07258, 2022.","title":"Clnode: Curriculum learning for node classification","authors":"Xiaowen Wei, Weiwei Liu, Yibing Zhan, Du Bo, Wenbin Hu","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Weinshall et al., 2018] Daphna Weinshall, Gad Cohen, and Dan Amir. Curriculum learning by transfer learning: Theory and ex- periments with deep networks. In ICML, 2018.","title":"Curriculum learning by transfer learning: Theory and experiments with deep networks","authors":"Daphna Weinshall, Gad Cohen, Dan Amir","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":58,"raw":"[Wu et al., 2020a] Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. A compre- hensive survey on graph neural networks. IEEE TNNLS, 2020.","title":"A comprehensive survey on graph neural networks","authors":"Wu","year":"2020","venue":"IEEE TNNLS","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Wu et al., 2020b] Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang. Connecting the dots: Multivariate time series forecasting with graph neural networks. In SIGKDD, 2020.","title":"Connecting the dots: Multivariate time series forecasting with graph neural networks","authors":"Wu","year":"2020","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Wu et al., 2022] Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. Graph neural networks in recommender systems: a sur- vey. ACM Computing Surveys, 2022.","title":"Graph neural networks in recommender systems: a survey","authors":"Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui","year":"2022","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Xu et al., 2019] Keyulu Xu, Weihua Hu, Jure Leskovec, and Ste- fanie Jegelka. How powerful are graph neural networks? ICLR, 2019.","title":"How powerful are graph neural networks? ICLR","authors":"Keyulu Xu, Weihua Hu, Jure Leskovec, Stefanie Jegelka","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Ying et al., 2018] Rex Ying, Ruining He, Kaifeng Chen, Pong Ek- sombatchai, William L Hamilton, and Jure Leskovec. Graph con- volutional neural networks for web-scale recommender systems. In SIGKDD, 2018.","title":"Graph convolutional neural networks for web-scale recommender systems","authors":"Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, Jure Leskovec","year":"2018","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[You et al., 2020] Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. Graph contrastive learning with augmentations. NeurIPS, 2020.","title":"Graph contrastive learning with augmentations","authors":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":64,"raw":"[Zhang and Tong, 2016] Si Zhang and Hanghang Tong. Final: Fast attributed network alignment. In SIGKDD, 2016.","title":"Final: Fast attributed network alignment","authors":"Si Zhang, Hanghang Tong","year":"2016","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":65,"raw":"[Zhang et al., 2020] Ziwei Zhang, Peng Cui, and Wenwu Zhu. Deep learning on graphs: A survey. IEEE TKDE, 2020.","title":"Deep learning on graphs: A survey","authors":"Ziwei Zhang, Peng Cui, Wenwu Zhu","year":"2020","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[Zhang et al., 2022a] Chuxu Zhang, Kaize Ding, Jundong Li, Xi- angliang Zhang, Yanfang Ye, Nitesh V Chawla, and Huan Liu. Few-shot learning on graphs: A survey. IJCAI, 2022.","title":"Few-shot learning on graphs: A survey","authors":"Zhang","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Zhang et al., 2022b] Zeyang Zhang, Ziwei Zhang, Xin Wang, and Wenwu Zhu. Learning to solve travelling salesman problem with hardness-adaptive curriculum. In AAAI, 2022.","title":"Learning to solve travelling salesman problem with hardness-adaptive curriculum","authors":"Zhang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Zhang et al., 2023] Zheng Zhang, Junxiang Wang, and Liang Zhao. Relational curriculum learning for graph neural networks, 2023.","title":"Relational curriculum learning for graph neural networks","authors":"Zheng Zhang, Junxiang Wang, Liang Zhao","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Zhou et al., 2022a] Dawei Zhou, Lecheng Zheng, Dongqi Fu, Ji- awei Han, and Jingrui He. Mentorgnn: deriving curriculum for pre-training gnns. In CIKM, 2022.","title":"Mentorgnn: deriving curriculum for pre-training gnns","authors":"Zhou","year":"2022","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":70,"raw":"[Zhou et al., 2022b] Yuwei Zhou, Hong Chen, Zirui Pan, Chuanhao Yan, Fanqi Lin, Xin Wang, and Wenwu Zhu. Curml: A curricu- lum machine learning library. In ACM MM, 2022.","title":"Curml: A curriculum machine learning library","authors":"Zhou","year":"2022","venue":"ACM MM","doi":null,"url":null,"internal_url":null},{"position":71,"raw":"[Zhu et al., 2021] Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi. Shift-robust gnns: Overcoming the limitations of localized graph training data. NeurIPS, 2021.","title":"Shift-robust gnns: Overcoming the limitations of localized graph training data","authors":"Zhu","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}