{"type":"ScholarlyArticle","title":"A Survey of Data-Efficient Graph Learning","authors":[{"name":"Wei Ju","slug":"wei-ju","given":"Wei","family":"Ju","affiliation":"School of Computer Science , Peking University ,"},{"name":"Siyu Yi","slug":"siyu-yi","given":"Siyu","family":"Yi","affiliation":"School of Statistics and Data Science , Nankai University ,"},{"name":"Yifan Wang","slug":"yifan-wang","given":"Yifan","family":"Wang","affiliation":"School of Information Technology & Management , University of International Business and Economics ,"},{"name":"Qingqing Long","slug":"qingqing-long","given":"Qingqing","family":"Long","affiliation":"School of Computer Science , Peking University ,"},{"name":"Junyu Luo","slug":"junyu-luo","given":"Junyu","family":"Luo","affiliation":"School of Computer Science , Peking University ,"},{"name":"Zhiping Xiao","slug":"zhiping-xiao","given":"Zhiping","family":"Xiao","affiliation":"Department of Computer Science , University of California Los Angeles"},{"name":"Ming Zhang","slug":"ming-zhang","given":"Ming","family":"Zhang","affiliation":"School of Computer Science , Peking University ,"}],"abstract":"Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural networks demonstrate proficiency in modeling this type of data, their success is often reliant on significant amounts of labeled data, posing a challenge in practical scenarios with limited annotation resources. To tackle this problem, tremendous efforts have been devoted to enhancing graph machine learning performance under low-resource settings by exploring various approaches to minimal supervision. In this paper, we introduce a novel concept of Data-Efficient Graph Learning (DEGL) as a research frontier, and present the first survey that summarizes the current progress of DEGL. We initiate by highlighting the challenges inherent in training models with large labeled data, paving the way for our exploration into DEGL. Next, we systematically review recent advances on this topic from several key aspects, including self-supervised graph learning, semi-supervised graph learning, and few-shot graph learning. Also, we state promising directions for future research, contributing to the evolution of graph machine learning.","doi":"10.24963/ijcai.2024/896","url":"https://ijcai.org/proceedings/2024/896","pdf":"https://ijcai.org/proceedings/2024/0896.pdf","markdown":"https://ijcai.org/proceedings/2024/896.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/896","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":"Survey Track","track":"Survey Track","number":896,"pages":{"first":8104,"last":8113},"keywords":["Machine Learning: ML: Weakly supervised learning","Data Mining: DM: Mining graphs","Machine Learning: ML: Self-supervised Learning","Machine Learning: ML: Few-shot learning","Machine Learning: ML: Semi-supervised learning"],"videos":[],"references":[{"position":1,"raw":"[Bo et al., 2020] Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, and Peng Cui. Structural deep clustering network. In WWW, 2020.","title":"Structural deep clustering network","authors":"Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, Peng Cui","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cai et al., 2023] Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, and Kun Zhang. Graph domain adapta- tion: A generative view. TKDD, 2023.","title":"Graph domain adaptation: A generative view","authors":"Cai","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chauhan et al., 2020] Jatin Chauhan, Deepak Nathani, and Manohar Kaul. Few-shot learning on graphs via super- classes based on graph spectral measures. In ICLR, 2020.","title":"Few-shot learning on graphs via superclasses based on graph spectral measures","authors":"Jatin Chauhan, Deepak Nathani, Manohar Kaul","year":"2020","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dai et al., 2019] Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, and Dan Wang. Graph transfer learning via ad- versarial domain adaptation with graph convolution. TKDE, 2019.","title":"Graph transfer learning via adversarial domain adaptation with graph convolution","authors":"Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, Dan Wang","year":"2019","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ding et al., 2020] Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu. Graph prototypical networks for few-shot learning on attributed networks. In CIKM, 2020.","title":"Graph prototypical networks for few-shot learning on attributed networks","authors":"Ding","year":"2020","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Finn et al., 2017] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017.","title":"Model-agnostic meta-learning for fast adaptation of deep networks","authors":"Finn","year":"2017","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Grohs and Voigtlaender, 2021] Philipp Grohs and Felix Voigtlaender. Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neu- ral network approximation spaces. arXiv preprint arXiv:2104.02746, 2021.","title":"Proof of the theory-to-practice gap in deep learning via sampling complexity bounds for neural network approximation spaces","authors":"Voigtlaender Grohs, Philipp Grohs, Felix Voigtlaender","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hao et al., 2020] Zhongkai Hao, Chengqiang Lu, Zhenya Huang, Hao Wang, Zheyuan Hu, Qi Liu, Enhong Chen, and Cheekong Lee. Asgn: An active semi-supervised graph neural network for molecular property prediction. In KDD, 2020.","title":"Asgn: An active semi-supervised graph neural network for molecular property prediction","authors":"Hao","year":"2020","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hu et al., 2019] Ziniu Hu, Changjun Fan, Ting Chen, Kai- Wei Chang, and Yizhou Sun. Pre-training graph neural networks for generic structural feature extraction. arXiv preprint arXiv:1905.13728, 2019.","title":"Pre-training graph neural networks for generic structural feature extraction","authors":"Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, Yizhou Sun","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Jin et al., 2020] Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang. Self- supervised learning on graphs: Deep insights and new di- rection. arXiv preprint arXiv:2006.10141, 2020.","title":"Selfsupervised learning on graphs: Deep insights and new direction","authors":"Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jin et al., 2021] Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, and Jiliang Tang. Node similarity preserving graph convolutional networks. In WSDM, 2021.","title":"Node similarity preserving graph convolutional networks","authors":"Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang","year":"2021","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Jin et al., 2023] Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, and Neil Shah. Empowering graph repre- sentation learning with test-time graph transformation. In ICLR, 2023.","title":"Empowering graph representation learning with test-time graph transformation","authors":"Wei Jin, Tong Zhao, Jiayuan Ding, Yozen Liu, Jiliang Tang, Neil Shah","year":"2023","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Ju et al., 2022a] Wei Ju, Xiao Luo, Meng Qu, Yifan Wang, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang. Tgnn: A joint semi-supervised framework for graph- level classification. In IJCAI, 2022.","title":"Tgnn: A joint semi-supervised framework for graphlevel classification","authors":"Ju","year":"2022","venue":"IJCAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Ju et al., 2022b] Wei Ju, Junwei Yang, Meng Qu, Weiping Song, Jianhao Shen, and Ming Zhang. Kgnn: Harnessing kernel-based networks for semi-supervised graph classifi- cation. In WSDM, 2022.","title":"Kgnn: Harnessing kernel-based networks for semi-supervised graph classification","authors":"Ju","year":"2022","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Ju et al., 2023a] Wei Ju, Yiyang Gu, Binqi Chen, Gongbo Sun, Yifang Qin, Xingyuming Liu, Xiao Luo, and Ming Zhang. Glcc: A general framework for graph-level cluster- ing. In AAAI, 2023.","title":"Glcc: A general framework for graph-level clustering","authors":"Ju","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ju et al., 2023b] Wei Ju, Zequn Liu, Yifang Qin, Bin Feng, Chen Wang, Zhihui Guo, Xiao Luo, and Ming Zhang. Few- shot molecular property prediction via hierarchically struc- tured learning on relation graphs. Neural Networks, 2023.","title":"Fewshot molecular property prediction via hierarchically structured learning on relation graphs","authors":"Ju","year":"2023","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ju et al., 2023c] Wei Ju, Yifang Qin, Siyu Yi, Zhengyang Mao, Kangjie Zheng, Luchen Liu, Xiao Luo, and Ming Zhang. Zero-shot node classification with graph contrastive embedding network. TMLR, 2023.","title":"Zero-shot node classification with graph contrastive embedding network","authors":"Ju","year":"2023","venue":"TMLR","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ju et al., 2024a] Wei Ju, Zheng Fang, Yiyang Gu, Zequn Liu, Qingqing Long, Ziyue Qiao, Yifang Qin, Jianhao Shen, Fang Sun, Zhiping Xiao, et al. A comprehensive survey on deep graph representation learning. Neural Networks, 2024.","title":"A comprehensive survey on deep graph representation learning","authors":"Ju","year":"2024","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Ju et al., 2024b] Wei Ju, Yifan Wang, Yifang Qin, Zhengyang Mao, Zhiping Xiao, Junyu Luo, Junwei Yang, Yiyang Gu, Dongjie Wang, Qingqing Long, et al. Towards graph contrastive learning: A survey and beyond. arXiv preprint arXiv:2405.11868, 2024.","title":"Towards graph contrastive learning: A survey and beyond","authors":"Ju","year":"2024","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Kim and Oh, 2021] Dongkwan Kim and Alice Oh. How to find your friendly neighborhood: Graph attention design with self-supervision. ICLR, 2021.","title":"Dongkwan Kim and Alice Oh. How to find your friendly neighborhood: Graph attention design with self-supervision","authors":"Oh Kim","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Kipf and Welling, 2017] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In ICLR, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Li et al., 2019] Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang. Semi-supervised graph classification: A hierarchical graph perspective. In WWW, 2019.","title":"Semi-supervised graph classification: A hierarchical graph perspective","authors":"Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang","year":"2019","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Liu et al., 2020] Zequn Liu, Ruiyi Zhang, Yiping Song, and Ming Zhang. When does maml work the best? an empirical study on model-agnostic meta-learning in nlp applications. arXiv preprint arXiv:2005.11700, 2020.","title":"When does maml work the best? an empirical study on model-agnostic meta-learning in nlp applications","authors":"Liu","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Liu et al., 2021] Yujia Liu, Kang Zeng, Haiyang Wang, Xin Song, and Bin Zhou. Content matters: A gnn-based model combined with text semantics for social network cascade prediction. In PAKDD, 2021.","title":"Content matters: A gnn-based model combined with text semantics for social network cascade prediction","authors":"Yujia Liu, Kang Zeng, Haiyang Wang, Xin Song, Bin Zhou","year":"2021","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Liu et al., 2022] Yue Liu, Wenxuan Tu, Sihang Zhou, Xin- wang Liu, Linxuan Song, Xihong Yang, and En Zhu. Deep graph clustering via dual correlation reduction. In AAAI, 2022.","title":"Deep graph clustering via dual correlation reduction","authors":"Yue Liu, Wenxuan Tu, Sihang Zhou, Xinwang Liu, Linxuan Song, Xihong Yang, En Zhu","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Liu et al., 2023a] Shikun Liu, Tianchun Li, Yongbin Feng, Nhan Tran, Han Zhao, Qiang Qiu, and Pan Li. Structural re-weighting improves graph domain adaptation. In ICML, 2023.","title":"Structural re-weighting improves graph domain adaptation","authors":"Liu","year":"2023","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2023b] Yue Liu, Xihong Yang, Sihang Zhou, Xin- wang Liu, Siwei Wang, Ke Liang, Wenxuan Tu, and Liang Li. Simple contrastive graph clustering. TNNLS, 2023.","title":"Simple contrastive graph clustering","authors":"Liu","year":"2023","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Luo et al., 2022a] Xiao Luo, Wei Ju, Meng Qu, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang. Dual- graph: Improving semi-supervised graph classification via dual contrastive learning. In ICDE, 2022.","title":"Dualgraph: Improving semi-supervised graph classification via dual contrastive learning","authors":"Luo","year":"2022","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Luo et al., 2022b] Xiao Luo, Wei Ju, Meng Qu, Yiyang Gu, Chong Chen, Minghua Deng, Xian-Sheng Hua, and Ming Zhang. Clear: Cluster-enhanced contrast for self- supervised graph representation learning. TNNLS, 2022.","title":"Clear: Cluster-enhanced contrast for selfsupervised graph representation learning","authors":"Luo","year":"2022","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Luo et al., 2023a] Xiao Luo, Wei Ju, Yiyang Gu, Zhengyang Mao, Luchen Liu, Yuhui Yuan, and Ming Zhang. Self- supervised graph-level representation learning with adver- sarial contrastive learning. TKDD, 2023.","title":"Selfsupervised graph-level representation learning with adversarial contrastive learning","authors":"Luo","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Luo et al., 2023b] Xiao Luo, Yusheng Zhao, Yifang Qin, Wei Ju, and Ming Zhang. Towards semi-supervised univer- sal graph classification. TKDE, 2023.","title":"Towards semi-supervised universal graph classification","authors":"Luo","year":"2023","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ma et al., 2020] Ning Ma, Jiajun Bu, Jieyu Yang, Zhen Zhang, Chengwei Yao, Zhi Yu, Sheng Zhou, and Xifeng Yan. Adaptive-step graph meta-learner for few-shot graph classification. In CIKM, 2020.","title":"Adaptive-step graph meta-learner for few-shot graph classification","authors":"Ning Ma, Jiajun Bu, Jieyu Yang, Zhen Zhang, Chengwei Yao, Zhi Yu, Sheng Zhou, Xifeng Yan","year":"2020","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Pan et al., 2019] Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, and Chengqi Zhang. Learning graph embedding with adversarial training methods. TCYB, 2019.","title":"Learning graph embedding with adversarial training methods","authors":"Pan","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Park et al., 2022] Namyong Park, Ryan Rossi, Eunyee Koh, Iftikhar Ahamath Burhanuddin, Sungchul Kim, Fan Du, Nesreen Ahmed, and Christos Faloutsos. Cgc: Contrastive graph clustering forcommunity detection and tracking. In WWW, 2022.","title":"Cgc: Contrastive graph clustering forcommunity detection and tracking","authors":"Park","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Peng et al., 2020] Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, and Qinghua Zheng. Self-supervised graph representation learning via global context prediction. arXiv preprint arXiv:2003.01604, 2020.","title":"Self-supervised graph representation learning via global context prediction","authors":"Zhen Peng, Yixiang Dong, Minnan Luo, Xiao-Ming Wu, Qinghua Zheng","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Shen et al., 2020] Xiao Shen, Quanyu Dai, Fu-lai Chung, Wei Lu, and Kup-Sze Choi. Adversarial deep network embedding for cross-network node classification. In AAAI, 2020.","title":"Adversarial deep network embedding for cross-network node classification","authors":"Xiao Shen, Quanyu Dai, Fu-Lai Chung, Wei Lu, Kup-Sze Choi","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Snell et al., 2017] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017.","title":"Prototypical networks for few-shot learning","authors":"Jake Snell, Kevin Swersky, Richard Zemel","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Sohn et al., 2020] Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li. Fixmatch: Simplifying semi-supervised learning with con- sistency and confidence. In NeurIPS, 2020.","title":"Fixmatch: Simplifying semi-supervised learning with consistency and confidence","authors":"Sohn","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":39,"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 ICLR, 2020.","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":"ICLR","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Sun et al., 2023] Li Sun, Feiyang Wang, Junda Ye, Hao Peng, and Philip S Yu. Congregate: contrastive graph clustering in curvature spaces. In IJCAI, 2023.","title":"Congregate: contrastive graph clustering in curvature spaces","authors":"Li Sun, Feiyang Wang, Junda Ye, Hao Peng, Philip S Yu","year":"2023","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2023/255"},{"position":41,"raw":"[Tan et al., 2022] Zhen Tan, Kaize Ding, Ruocheng Guo, and Huan Liu. Graph few-shot class-incremental learning. In WSDM, 2022.","title":"Graph few-shot class-incremental learning","authors":"Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu","year":"2022","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Tan et al., 2023] Zhen Tan, Ruocheng Guo, Kaize Ding, and Huan Liu. Virtual node tuning for few-shot node classifica- tion. In KDD, 2023.","title":"Virtual node tuning for few-shot node classification","authors":"Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu","year":"2023","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Tao et al., 2019] Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, and Yun Fu. Adversarial graph embedding for ensemble clustering. In IJCAI, 2019.","title":"Adversarial graph embedding for ensemble clustering","authors":"Zhiqiang Tao, Hongfu Liu, Jun Li, Zhaowen Wang, Yun Fu","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/494"},{"position":44,"raw":"[Tu et al., 2021] Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, and Jieren Cheng. Deep fusion clustering network. In AAAI, 2021.","title":"Deep fusion clustering network","authors":"Wenxuan Tu, Sihang Zhou, Xinwang Liu, Xifeng Guo, Zhiping Cai, En Zhu, Jieren Cheng","year":"2021","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Veličković et al., 2018] Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. Deep graph infomax. arXiv preprint arXiv:1809.10341, 2018.","title":"Deep graph infomax","authors":"Petar Veličković, William Fedus, Pietro William L Hamilton, Yoshua Liò, Devon Bengio, Hjelm","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Wang and Leskovec, 2021] Hongwei Wang and Jure Leskovec. Combining graph convolutional neural networks and label propagation. TOIS, 2021.","title":"Combining graph convolutional neural networks and label propagation","authors":"Hongwei Wang, Jure Leskovec","year":"2021","venue":"TOIS","doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Wang et al., 2017] Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, and Jing Jiang. Mgae: Marginalized graph autoencoder for graph clustering. In CIKM, 2017.","title":"Mgae: Marginalized graph autoencoder for graph clustering","authors":"Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang","year":"2017","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Wang et al., 2019] Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, and Chengqi Zhang. Attributed graph clustering: A deep attentional embedding approach. arXiv preprint arXiv:1906.06532, 2019.","title":"Attributed graph clustering: A deep attentional embedding approach","authors":"Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2019/509"},{"position":49,"raw":"[Wang et al., 2020] Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, and Qinghua Zheng. Graph few-shot learning with attribute matching. In CIKM, 2020.","title":"Graph few-shot learning with attribute matching","authors":"Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li, Qinghua Zheng","year":"2020","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Wang et al., 2021] Xiao Wang, Nian Liu, Hui Han, and Chuan Shi. Self-supervised heterogeneous graph neural network with co-contrastive learning. In KDD, 2021.","title":"Self-supervised heterogeneous graph neural network with co-contrastive learning","authors":"Xiao Wang, Nian Liu, Hui Han, Chuan Shi","year":"2021","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Wang et al., 2022a] Song Wang, Chen Chen, and Jundong Li. Graph few-shot learning with task-specific structures. In NeurIPS, 2022.","title":"Graph few-shot learning with task-specific structures","authors":"Wang","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Wang et al., 2022b] Yifan Wang, Yiping Song, Shuai Li, Chaoran Cheng, Wei Ju, Ming Zhang, and Sheng Wang. Disencite: Graph-based disentangled representation learn- ing for context-specific citation generation. In AAAI, 2022.","title":"Disencite: Graph-based disentangled representation learning for context-specific citation generation","authors":"Wang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Wang et al., 2022c] Yiqi Wang, Chaozhuo Li, Wei Jin, Rui Li, Jianan Zhao, Jiliang Tang, and Xing Xie. Test- time training for graph neural networks. arXiv preprint arXiv:2210.08813, 2022.","title":"Testtime training for graph neural networks","authors":"Wang","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Wu et al., 2020] Man Wu, Shirui Pan, Chuan Zhou, Xiaojun Chang, and Xingquan Zhu. Unsupervised domain adaptive graph convolutional networks. In WWW, 2020.","title":"Unsupervised domain adaptive graph convolutional networks","authors":"Wu","year":"2020","venue":"WWW","doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Wu et al., 2023] Jun Wu, Jingrui He, and Elizabeth Ainsworth. Non-iid transfer learning on graphs. In AAAI, 2023.","title":"Non-iid transfer learning on graphs","authors":"Wu","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Xia et al., 2022] Wei Xia, Qianqian Wang, Quanxue Gao, Ming Yang, and Xinbo Gao. Self-consistent contrastive attributed graph clustering with pseudo-label prompt. TMM, 2022.","title":"Self-consistent contrastive attributed graph clustering with pseudo-label prompt","authors":"Wei Xia, Qianqian Wang, Quanxue Gao, Ming Yang, Xinbo Gao","year":"2022","venue":"TMM","doi":null,"url":null,"internal_url":null},{"position":57,"raw":"[Yang et al., 2020] Liang Yang, Yuexue Wang, Junhua Gu, Chuan Wang, Xiaochun Cao, and Yuanfang Guo. Jane: Jointly adversarial network embedding. In IJCAI, 2020.","title":"Jane: Jointly adversarial network embedding","authors":"Liang Yang, Yuexue Wang, Junhua Gu, Chuan Wang, Xiaochun Cao, Yuanfang Guo","year":"2020","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2020/192"},{"position":58,"raw":"[Yang et al., 2023] Junwei Yang, Hanwen Xu, Srbuhi Mir- zoyan, Tong Chen, Zixuan Liu, Wei Ju, Luchen Liu, Ming Zhang, and Sheng Wang. Poisoning scientific knowledge using large language models. bioRxiv, 2023.","title":"Poisoning scientific knowledge using large language models","authors":"Junwei Yang, Hanwen Xu, Srbuhi Mirzoyan, Tong Chen, Zixuan Liu, Wei Ju, Luchen Liu, Ming Zhang, Sheng Wang","year":"2023","venue":"bioRxiv","doi":null,"url":null,"internal_url":null},{"position":59,"raw":"[Yao et al., 2020] Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, and Zhenhui Li. Graph few-shot learning via knowledge transfer. In AAAI, 2020.","title":"Graph few-shot learning via knowledge transfer","authors":"Yao","year":"2020","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":60,"raw":"[Yasunaga et al., 2021] Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. Qa-gnn: Reasoning with language models and knowledge graphs for question answering. arXiv preprint arXiv:2104.06378, 2021.","title":"Qa-gnn: Reasoning with language models and knowledge graphs for question answering","authors":"Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":61,"raw":"[Yi et al., 2023] Siyu Yi, Wei Ju, Yifang Qin, Xiao Luo, Luchen Liu, Yongdao Zhou, and Ming Zhang. Redundancy- free self-supervised relational learning for graph clustering. TNNLS, 2023.","title":"Redundancyfree self-supervised relational learning for graph clustering","authors":"Siyu Yi, Wei Ju, Yifang Qin, Xiao Luo, Luchen Liu, Yongdao Zhou, Ming Zhang","year":"2023","venue":"TNNLS","doi":null,"url":null,"internal_url":null},{"position":62,"raw":"[Yin et al., 2022] Nan Yin, Li Shen, Baopu Li, Mengzhu Wang, Xiao Luo, Chong Chen, Zhigang Luo, and Xian- Sheng Hua. Deal: An unsupervised domain adaptive frame- work for graph-level classification. In MM, 2022.","title":"Deal: An unsupervised domain adaptive framework for graph-level classification","authors":"Nan Yin, Li Shen, Baopu Li, Mengzhu Wang, Xiao Luo, Chong Chen, Zhigang Luo, Xian-Sheng Hua","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":63,"raw":"[Yin et al., 2023] Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, and Xiao Luo. Coco: A coupled contrastive framework for unsu- pervised domain adaptive graph classification. In ICML, 2023.","title":"Coco: A coupled contrastive framework for unsupervised domain adaptive graph classification","authors":"Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian-Sheng Hua, Xiao Luo","year":"2023","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":64,"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 NeurIPS, 2020.","title":"Graph contrastive learning with augmentations","authors":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, Yang Shen","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":65,"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, 2021.","title":"Graph contrastive learning automated","authors":"You","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":66,"raw":"[You et al., 2023] Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. Graph domain adaptation via theory- grounded spectral regularization. In ICLR, 2023.","title":"Graph domain adaptation via theorygrounded spectral regularization","authors":"You","year":"2023","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":67,"raw":"[Yu et al., 2022] Tianyuan Yu, Sen He, Yi-Zhe Song, and Tao Xiang. Hybrid graph neural networks for few-shot learning. In AAAI, 2022.","title":"Hybrid graph neural networks for few-shot learning","authors":"Tianyuan Yu, Sen He, Yi-Zhe Song, Tao Xiang","year":"2022","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":68,"raw":"[Yu et al., 2023] Zhuohan Yu, Yanchi Su, Yifu Lu, Yuning Yang, Fuzhou Wang, Shixiong Zhang, Yi Chang, Ka-Chun Wong, and Xiangtao Li. Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scmgca. Nature Communi- cations, 2023.","title":"Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scmgca","authors":"Yu","year":"2023","venue":"Nature Communications","doi":null,"url":null,"internal_url":null},{"position":69,"raw":"[Yue et al., 2022] Han Yue, Chunhui Zhang, Chuxu Zhang, and Hongfu Liu. Label-invariant augmentation for semi- supervised graph classification. In NeurIPS, 2022.","title":"Label-invariant augmentation for semisupervised graph classification","authors":"Han Yue, Chunhui Zhang, Chuxu Zhang, Hongfu Liu","year":"2022","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":70,"raw":"[Zhang et al., 2020] Yizhou Zhang, Guojie Song, Lun Du, Shuwen Yang, and Yilun Jin. Domain adaptive network embedding. arXiv preprint arXiv:1906.00684, 2020.","title":"Domain adaptive network embedding","authors":"Zhang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":71,"raw":"[Zhang et al., 2021] Xiaowen Zhang, Yuntao Du, Rongbiao Xie, and Chongjun Wang. Adversarial separation network for cross-network node classification. In CIKM, 2021.","title":"Adversarial separation network for cross-network node classification","authors":"Xiaowen Zhang, Yuntao Du, Rongbiao Xie, Chongjun Wang","year":"2021","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":72,"raw":"[Zhao et al., 2021] Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, and Cheng Deng. Graph debiased contrastive learn- ing with joint representation clustering. In IJCAI, 2021.","title":"Graph debiased contrastive learning with joint representation clustering","authors":"Han Zhao, Xu Yang, Zhenru Wang, Erkun Yang, Cheng Deng","year":"2021","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2021/473"},{"position":73,"raw":"[Zhou et al., 2019] Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng. Meta-gnn: On few-shot node classification in graph meta-learning. In CIKM, 2019.","title":"Meta-gnn: On few-shot node classification in graph meta-learning","authors":"Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, Ji Geng","year":"2019","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":74,"raw":"[Zhu et al., 2020] Yanqiao Zhu, Yichen Xu, Feng Yu, Shu Wu, and Liang Wang. Cagnn: Cluster-aware graph neural networks for unsupervised graph representation learning. arXiv preprint arXiv:2009.01674, 2020.","title":"Cagnn: Cluster-aware graph neural networks for unsupervised graph representation learning","authors":"Yanqiao Zhu, Yichen Xu, Feng Yu, Shu Wu, Liang Wang","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":75,"raw":"[Zhu, 2005] Xiaojin Zhu. Semi-supervised learning with graphs. Carnegie Mellon University, 2005.","title":"Semi-supervised learning with graphs","authors":"Xiaojin Zhu","year":"2005","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}