{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:23:36Z","timestamp":1771064616032,"version":"3.50.1"},"reference-count":76,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2021,12,30]],"date-time":"2021-12-30T00:00:00Z","timestamp":1640822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["SIGKDD Explor. Newsl."],"published-print":{"date-parts":[[2021,12,30]]},"abstract":"<jats:p>Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.<\/jats:p>","DOI":"10.1145\/3510374.3510379","type":"journal-article","created":{"date-parts":[[2022,1,3]],"date-time":"2022-01-03T11:05:40Z","timestamp":1641207940000},"page":"13-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["MetaLearning with Graph Neural Networks"],"prefix":"10.1145","volume":"23","author":[{"given":"Debmalya","family":"Mandal","sequence":"first","affiliation":[{"name":"Columbia University, New York, NY, USA"}]},{"given":"Sourav","family":"Medya","sequence":"additional","affiliation":[{"name":"Northwestern University"}]},{"given":"Brian","family":"Uzzi","sequence":"additional","affiliation":[{"name":"Northwestern University"}]},{"given":"Charu","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"IBM T. J. Watson Research Center, Yorktown Heights, NY, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,1,3]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Dong Bok Lee, and Sung Ju Hwang. \"Learning to extrapolate knowledge: Transductive few-shot out-ofgraph link prediction","author":"Baek Jinheon","year":"2020","unstructured":"Jinheon Baek , Dong Bok Lee, and Sung Ju Hwang. \"Learning to extrapolate knowledge: Transductive few-shot out-ofgraph link prediction \". In : NeurIPS ( 2020 ). Jinheon Baek, Dong Bok Lee, and Sung Ju Hwang. \"Learning to extrapolate knowledge: Transductive few-shot out-ofgraph link prediction\". In: NeurIPS (2020)."},{"key":"e_1_2_1_2_1","volume-title":"andWeiWang. \"SimGNN: A Neural Network Approach to Fast Graph Similarity Computation","author":"Bai Yunsheng","year":"2019","unstructured":"Yunsheng Bai , Hao Ding , Song Bian , Ting Chen , Yizhou Sun , andWeiWang. \"SimGNN: A Neural Network Approach to Fast Graph Similarity Computation \". In : WSDM. 2019 . Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, andWeiWang. \"SimGNN: A Neural Network Approach to Fast Graph Similarity Computation\". In: WSDM. 2019."},{"key":"e_1_2_1_3_1","volume-title":"arXiv preprint arXiv:2002.03129","author":"Bai Yunsheng","year":"2020","unstructured":"Yunsheng Bai , Derek Xu , Alex Wang , Ken Gu , Xueqing Wu , Agustin Marinovic , Christopher Ro , Yizhou Sun , and Wei Wang . \" Fast detection of maximum common subgraph via deep q-learning\". In: arXiv preprint arXiv:2002.03129 ( 2020 ). Yunsheng Bai, Derek Xu, Alex Wang, Ken Gu, Xueqing Wu, Agustin Marinovic, Christopher Ro, Yizhou Sun, and Wei Wang. \"Fast detection of maximum common subgraph via deep q-learning\". In: arXiv preprint arXiv:2002.03129 (2020)."},{"key":"e_1_2_1_4_1","first-page":"424","volume-title":"ICML.","author":"Balcan Maria-Florina","year":"2019","unstructured":"Maria-Florina Balcan , Mikhail Khodak , and Ameet Talwalkar . \"Provable guarantees for gradient-based meta-learning \". In: ICML. 2019 , pp. 424 -- 433 . Maria-Florina Balcan, Mikhail Khodak, and Ameet Talwalkar. \"Provable guarantees for gradient-based meta-learning\". In: ICML. 2019, pp. 424--433."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.731"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"e_1_2_1_7_1","volume-title":"Few shot link prediction via meta learning\". In: arXiv preprint arXiv:1912.09867","author":"Bose Avishek Joey","year":"2019","unstructured":"Avishek Joey Bose , Ankit Jain , Piero Molino , and William L Hamilton . \"Meta-graph : Few shot link prediction via meta learning\". In: arXiv preprint arXiv:1912.09867 ( 2019 ). Avishek Joey Bose, Ankit Jain, Piero Molino, and William L Hamilton. \"Meta-graph: Few shot link prediction via meta learning\". In: arXiv preprint arXiv:1912.09867 (2019)."},{"key":"e_1_2_1_8_1","volume-title":"arXiv preprint arXiv:2012.06755","author":"Buffelli Davide","year":"2020","unstructured":"Davide Buffelli and Fabio Vandin . \" A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings\". In: arXiv preprint arXiv:2012.06755 ( 2020 ). Davide Buffelli and Fabio Vandin. \"A Meta-Learning Approach for Graph Representation Learning in Multi-Task Settings\". In: arXiv preprint arXiv:2012.06755 (2020)."},{"key":"e_1_2_1_9_1","volume-title":"AAAI 30.1","author":"Cao Shaosheng","year":"2016","unstructured":"Shaosheng Cao , Wei Lu , and Qiongkai Xu. \"Deep neural networks for learning graph representations\". In: AAAI 30.1 ( 2016 ). Shaosheng Cao, Wei Lu, and Qiongkai Xu. \"Deep neural networks for learning graph representations\". In: AAAI 30.1 (2016)."},{"key":"e_1_2_1_10_1","volume-title":"arXiv preprint arXiv:2102.09544","author":"Cappart Quentin","year":"2021","unstructured":"Quentin Cappart , Didier Ch\u00b4etelat , Elias Khalil , Andrea Lodi , Christopher Morris , and Petar Veli\"ckovi \u00b4c . \" Combinatorial optimization and reasoning with graph neural networks\". In: arXiv preprint arXiv:2102.09544 ( 2021 ). Quentin Cappart, Didier Ch\u00b4etelat, Elias Khalil, Andrea Lodi, Christopher Morris, and Petar Veli\"ckovi\u00b4c. \"Combinatorial optimization and reasoning with graph neural networks\". In: arXiv preprint arXiv:2102.09544 (2021)."},{"key":"e_1_2_1_11_1","volume-title":"ICLR","author":"Chauhan Jatin","year":"2020","unstructured":"Jatin Chauhan , Deepak Nathani , and Manohar Kaul . \"Few- Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures\". In: ICLR ( 2020 ). Jatin Chauhan, Deepak Nathani, and Manohar Kaul. \"Few- Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures\". In: ICLR (2020)."},{"key":"e_1_2_1_12_1","first-page":"4208","article-title":"Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs","author":"Chen Mingyang","year":"2019","unstructured":"Mingyang Chen , Wen Zhang , Wei Zhang , Qiang Chen , and Huajun Chen . \" Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs \". In: EMNLP-IJCNLP. 2019 , pp. 4208 -- 4217 . Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, and Huajun Chen. \"Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs\". In: EMNLP-IJCNLP. 2019, pp. 4208--4217.","journal-title":"EMNLP-IJCNLP."},{"key":"e_1_2_1_13_1","volume-title":"arXiv:2105.02221","author":"Chua Kurtland","year":"2021","unstructured":"Kurtland Chua , Qi Lei , and Jason D Lee . \" How fine-tuning allows for effective meta-learning\". In: arXiv:2105.02221 ( 2021 ). Kurtland Chua, Qi Lei, and Jason D Lee. \"How fine-tuning allows for effective meta-learning\". In: arXiv:2105.02221 (2021)."},{"key":"e_1_2_1_14_1","first-page":"4883","volume-title":"IEEE TITS","volume":"23","author":"Cui Zhiyong","year":"2019","unstructured":"Zhiyong Cui , Kristian Henrickson , Ruimin Ke , and Yinhai Wang . \"Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting \". In: IEEE TITS ( 2019 ), pp. 4883 -- 4894 . SIGKDD Explorations Volume 23 , Issue 2 20 Zhiyong Cui, Kristian Henrickson, Ruimin Ke, and Yinhai Wang. \"Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting\". In: IEEE TITS (2019), pp. 4883--4894. SIGKDD Explorations Volume 23, Issue 2 20"},{"key":"e_1_2_1_15_1","volume-title":"NeurIPS","author":"Dai Hanjun","year":"2017","unstructured":"Hanjun Dai , Elias B Khalil , Yuyu Zhang , Bistra Dilkina , and Le Song . \" Learning combinatorial optimization algorithms over graphs\". In: NeurIPS ( 2017 ). Hanjun Dai, Elias B Khalil, Yuyu Zhang, Bistra Dilkina, and Le Song. \"Learning combinatorial optimization algorithms over graphs\". In: NeurIPS (2017)."},{"key":"e_1_2_1_16_1","first-page":"1566","article-title":"Learning-to-learn stochastic gradient descent with biased regularization","author":"Denevi Giulia","year":"2019","unstructured":"Giulia Denevi , Carlo Ciliberto , Riccardo Grazzi , and Massimiliano Pontil . \" Learning-to-learn stochastic gradient descent with biased regularization \". In: ICML. 2019 , pp. 1566 -- 1575 . Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, and Massimiliano Pontil. \"Learning-to-learn stochastic gradient descent with biased regularization\". In: ICML. 2019, pp. 1566-- 1575.","journal-title":"ICML."},{"key":"e_1_2_1_17_1","volume-title":"Pre-training of deep bidirectional transformers for language understanding","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . \"Bert : Pre-training of deep bidirectional transformers for language understanding \". In : NAACL-HLT ( 2019 ). Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. \"Bert: Pre-training of deep bidirectional transformers for language understanding\". In: NAACL-HLT (2019)."},{"key":"e_1_2_1_18_1","volume-title":"arXiv:2106.06873","author":"Ding Kaize","year":"2021","unstructured":"Kaize Ding , Jianling Wang , Jundong Li , James Caverlee , and Huan Liu . \"Weakly-supervised Graph Meta-learning for Few-shot Node Classification\". In: arXiv:2106.06873 ( 2021 ). Kaize Ding, Jianling Wang, Jundong Li, James Caverlee, and Huan Liu. \"Weakly-supervised Graph Meta-learning for Few-shot Node Classification\". In: arXiv:2106.06873 (2021)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411922"},{"key":"e_1_2_1_20_1","volume-title":"Proceedings of theWeb Conference 2021","author":"Ding Kaize","year":"2021","unstructured":"Kaize Ding , Qinghai Zhou , Hanghang Tong , and Huan Liu . \" Few-Shot Network Anomaly Detection via Cross-Network Meta-Learning\". In: Proceedings of theWeb Conference 2021 . 2021 , 2448--2456. Kaize Ding, Qinghai Zhou, Hanghang Tong, and Huan Liu. \"Few-Shot Network Anomaly Detection via Cross-Network Meta-Learning\". In: Proceedings of theWeb Conference 2021. 2021, 2448--2456."},{"key":"e_1_2_1_21_1","volume-title":"provably\". In: arXiv preprint arXiv:2002.09434","author":"Du Simon S","year":"2020","unstructured":"Simon S Du , Wei Hu , Sham M Kakade , Jason D Lee , and Qi Lei . \"Few-shot learning via learning the representation , provably\". In: arXiv preprint arXiv:2002.09434 ( 2020 ). Simon S Du, Wei Hu, Sham M Kakade, Jason D Lee, and Qi Lei. \"Few-shot learning via learning the representation, provably\". In: arXiv preprint arXiv:2002.09434 (2020)."},{"key":"e_1_2_1_22_1","volume-title":"arXiv preprint arXiv:1912.09893","author":"Errica Federico","year":"2019","unstructured":"Federico Errica , Marco Podda , Davide Bacciu , and Alessio Micheli . \" A fair comparison of graph neural networks for graph classification\". In: arXiv preprint arXiv:1912.09893 ( 2019 ). Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli. \"A fair comparison of graph neural networks for graph classification\". In: arXiv preprint arXiv:1912.09893 (2019)."},{"key":"e_1_2_1_23_1","first-page":"1126","article-title":"Modelagnostic meta-learning for fast adaptation of deep networks","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . \" Modelagnostic meta-learning for fast adaptation of deep networks \". In: ICML. 2017 , pp. 1126 -- 1135 . Chelsea Finn, Pieter Abbeel, and Sergey Levine. \"Modelagnostic meta-learning for fast adaptation of deep networks\". In: ICML. 2017, pp. 1126--1135.","journal-title":"ICML."},{"key":"e_1_2_1_24_1","first-page":"1920","article-title":"Online meta-learning","author":"Finn Chelsea","year":"2019","unstructured":"Chelsea Finn , Aravind Rajeswaran , Sham Kakade , and Sergey Levine . \" Online meta-learning \". In: ICML. 2019 , pp. 1920 -- 1930 . Chelsea Finn, Aravind Rajeswaran, Sham Kakade, and Sergey Levine. \"Online meta-learning\". In: ICML. 2019, pp. 1920-- 1930.","journal-title":"ICML."},{"key":"e_1_2_1_25_1","first-page":"3419","article-title":"Generalization and representational limits of graph neural networks","author":"Garg Vikas","year":"2020","unstructured":"Vikas Garg , Stefanie Jegelka , and Tommi Jaakkola . \" Generalization and representational limits of graph neural networks \". In: ICML. PMLR. 2020 , pp. 3419 -- 3430 . Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola. \"Generalization and representational limits of graph neural networks\". In: ICML. PMLR. 2020, pp. 3419--3430.","journal-title":"ICML. PMLR."},{"key":"e_1_2_1_26_1","volume-title":"NeurIPS","author":"Gasse Maxime","year":"2019","unstructured":"Maxime Gasse , Didier Ch\u00b4etelat , Nicola Ferroni , Laurent Charlin , and Andrea Lodi . \" Exact combinatorial optimization with graph convolutional neural networks\". In: NeurIPS ( 2019 ). Maxime Gasse, Didier Ch\u00b4etelat, Nicola Ferroni, Laurent Charlin, and Andrea Lodi. \"Exact combinatorial optimization with graph convolutional neural networks\". In: NeurIPS (2019)."},{"key":"e_1_2_1_27_1","volume-title":"The Web Conference","author":"Guo Zhichun","year":"2021","unstructured":"Zhichun Guo , Chuxu Zhang , Wenhao Yu , John Herr , Olaf Wiest , Meng Jiang , and Nitesh V Chawla . \" Few-Shot Graph Learning for Molecular Property Prediction\". In: The Web Conference ( 2021 ). Zhichun Guo, Chuxu Zhang, Wenhao Yu, John Herr, Olaf Wiest, Meng Jiang, and Nitesh V Chawla. \"Few-Shot Graph Learning for Molecular Property Prediction\". In: The Web Conference (2021)."},{"key":"e_1_2_1_28_1","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton , Zhitao Ying , and Jure Leskovec . \" Inductive representation learning on large graphs \". In: NeurIPS. 2017 , pp. 1024 -- 1034 . Will Hamilton, Zhitao Ying, and Jure Leskovec. \"Inductive representation learning on large graphs\". In: NeurIPS. 2017, pp. 1024--1034.","journal-title":"NeurIPS."},{"key":"e_1_2_1_29_1","volume-title":"Methods and applications\". In: arXiv preprint arXiv:1709.05584","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton , Rex Ying , and Jure Leskovec . \" Representation learning on graphs : Methods and applications\". In: arXiv preprint arXiv:1709.05584 ( 2017 ). William L Hamilton, Rex Ying, and Jure Leskovec. \"Representation learning on graphs: Methods and applications\". In: arXiv preprint arXiv:1709.05584 (2017)."},{"key":"e_1_2_1_30_1","volume-title":"A survey\". In: arXiv preprint arXiv:2004.05439","author":"Hospedales Timothy","year":"2020","unstructured":"Timothy Hospedales , Antreas Antoniou , Paul Micaelli , and Amos Storkey . \"Meta-learning in neural networks : A survey\". In: arXiv preprint arXiv:2004.05439 ( 2020 ). Timothy Hospedales, Antreas Antoniou, Paul Micaelli, and Amos Storkey. \"Meta-learning in neural networks: A survey\". In: arXiv preprint arXiv:2004.05439 (2020)."},{"key":"e_1_2_1_31_1","volume-title":"NeurIPS","author":"Huang Kexin","year":"2020","unstructured":"Kexin Huang and Marinka Zitnik . \" Graph meta learning via local subgraphs\". In: NeurIPS ( 2020 ). Kexin Huang and Marinka Zitnik. \"Graph meta learning via local subgraphs\". In: NeurIPS (2020)."},{"key":"e_1_2_1_32_1","volume-title":"arXiv preprint arXiv:2103.00771","author":"Hwang Dasol","year":"2021","unstructured":"Dasol Hwang , Jinyoung Park , Sunyoung Kwon , Kyung-Min Kim , Jung-Woo Ha , and Hyunwoo J Kim . \"Self-supervised Auxiliary Learning for Graph Neural Networks via Meta- Learning\". In: arXiv preprint arXiv:2103.00771 ( 2021 ). Dasol Hwang, Jinyoung Park, Sunyoung Kwon, Kyung-Min Kim, Jung-Woo Ha, and Hyunwoo J Kim. \"Self-supervised Auxiliary Learning for Graph Neural Networks via Meta- Learning\". In: arXiv preprint arXiv:2103.00771 (2021)."},{"key":"e_1_2_1_33_1","volume-title":"arXiv preprint arXiv:2103.03547","author":"Jiang Shunyu","year":"2021","unstructured":"Shunyu Jiang , Fuli Feng , Weijian Chen , Xiang Li , and Xiangnan He. \" Structure-Enhanced Meta-Learning For Few- Shot Graph Classification\". In: arXiv preprint arXiv:2103.03547 ( 2021 ). Shunyu Jiang, Fuli Feng, Weijian Chen, Xiang Li, and Xiangnan He. \"Structure-Enhanced Meta-Learning For Few- Shot Graph Classification\". In: arXiv preprint arXiv:2103.03547 (2021)."},{"key":"e_1_2_1_34_1","volume-title":"KDD.","author":"Kempe David","year":"2003","unstructured":"David Kempe , Jon Kleinberg , and \u00b4 Eva Tardos . \" Maximizing the spread of influence through a social network\". In: KDD. 2003 . David Kempe, Jon Kleinberg, and \u00b4 Eva Tardos. \"Maximizing the spread of influence through a social network\". In: KDD. 2003."},{"key":"e_1_2_1_35_1","volume-title":"Neural Information Processing Systems.","author":"Talwalkar A","year":"2019","unstructured":"MKhodak,MBalcan, and A Talwalkar . \" Adaptive Gradient- Based Meta-Learning Methods\". In: Neural Information Processing Systems. 2019 . MKhodak,MBalcan, and A Talwalkar. \"Adaptive Gradient- Based Meta-Learning Methods\". In: Neural Information Processing Systems. 2019."},{"key":"e_1_2_1_36_1","volume-title":"ICLR","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and MaxWelling. \"Semi-supervised classification with graph convolutional networks\". In: ICLR ( 2017 ). Thomas N Kipf and MaxWelling. \"Semi-supervised classification with graph convolutional networks\". In: ICLR (2017)."},{"key":"e_1_2_1_37_1","volume-title":"arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . \" Variational graph autoencoders\". In: arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. \"Variational graph autoencoders\". In: arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_2_1_38_1","volume-title":"NeurIPS","author":"Lan Lin","year":"2020","unstructured":"Lin Lan , PinghuiWang, Xuefeng Du , Kaikai Song , Jing Tao , and Xiaohong Guan . \" Node classification on graphs with few-shot novel labels via meta transformed network embedding\". In: NeurIPS ( 2020 ). Lin Lan, PinghuiWang, Xuefeng Du, Kaikai Song, Jing Tao, and Xiaohong Guan. \"Node classification on graphs with few-shot novel labels via meta transformed network embedding\". In: NeurIPS (2020)."},{"key":"e_1_2_1_39_1","volume-title":"ICLR","author":"Li Yujia","year":"2016","unstructured":"Yujia Li , Daniel Tarlow , Marc Brockschmidt , and Richard Zemel . \" Gated graph sequence neural networks\". In: ICLR ( 2016 ). Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. \"Gated graph sequence neural networks\". In: ICLR (2016)."},{"key":"e_1_2_1_40_1","volume-title":"NeurIPS","author":"Li Zhuwen","year":"2018","unstructured":"Zhuwen Li , Qifeng Chen , and Vladlen Koltun . \" Combinatorial optimization with graph convolutional networks and guided tree search\". In: NeurIPS ( 2018 ). Zhuwen Li, Qifeng Chen, and Vladlen Koltun. \"Combinatorial optimization with graph convolutional networks and guided tree search\". In: NeurIPS (2018)."},{"key":"e_1_2_1_41_1","volume-title":"ACL.","author":"Liu Xiaodong","year":"2019","unstructured":"Xiaodong Liu , Pengcheng He , Weizhu Chen , and Jianfeng Gao . \" Multi-Task Deep Neural Networks for Natural Language Understanding\". In: ACL. 2019 . Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. \"Multi-Task Deep Neural Networks for Natural Language Understanding\". In: ACL. 2019."},{"key":"e_1_2_1_42_1","volume-title":"AAAI","author":"Liu Zemin","year":"2021","unstructured":"Zemin Liu , Yuan Fang , Chenghao Liu , and Steven CH Hoi . \" Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph\". In: AAAI ( 2021 ). Zemin Liu, Yuan Fang, Chenghao Liu, and Steven CH Hoi. \"Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph\". In: AAAI (2021)."},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411910"},{"key":"e_1_2_1_44_1","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management.","author":"Ma Ning","year":"2020","unstructured":"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: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020 , 1055--1064. 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: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 1055--1064."},{"key":"e_1_2_1_45_1","volume-title":"Learning Budget-constrained Combinatorial Algorithms over Billionsized Graphs","author":"Manchanda Sahil","year":"2020","unstructured":"Sahil Manchanda , Akash Mittal , Anuj Dhawan , Sourav Medya , Sayan Ranu , and Ambuj Singh . \"GCOMB : Learning Budget-constrained Combinatorial Algorithms over Billionsized Graphs \". In : NeurIPS ( 2020 ). Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu, and Ambuj Singh. \"GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billionsized Graphs\". In: NeurIPS (2020)."},{"issue":"81","key":"e_1_2_1_46_1","first-page":"1","article-title":"The benefit of multitask representation learning","volume":"17","author":"Maurer Andreas","year":"2016","unstructured":"Andreas Maurer , Massimiliano Pontil , and Bernardino Romera- Paredes . \" The benefit of multitask representation learning \". In: Journal of Machine Learning Research 17 . 81 ( 2016 ), pp. 1 -- 32 . Andreas Maurer, Massimiliano Pontil, and Bernardino Romera- Paredes. \"The benefit of multitask representation learning\". In: Journal of Machine Learning Research 17.81 (2016), pp. 1--32.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_1_47_1","volume-title":"VLDB","author":"Medya Sourav","year":"2018","unstructured":"Sourav Medya , Jithin Vachery , Sayan Ranu , and Ambuj Singh . \" Noticeable network delay minimization via node upgrades\". In: VLDB ( 2018 ). Sourav Medya, Jithin Vachery, Sayan Ranu, and Ambuj Singh. \"Noticeable network delay minimization via node upgrades\". In: VLDB (2018)."},{"key":"e_1_2_1_48_1","volume-title":"CoRL.","author":"Nagabandi Anusha","year":"2020","unstructured":"Anusha Nagabandi , Kurt Konolige , Sergey Levine , and Vikash Kumar . \" Deep dynamics models for learning dexterous manipulation\". In: CoRL. 2020 . Anusha Nagabandi, Kurt Konolige, Sergey Levine, and Vikash Kumar. \"Deep dynamics models for learning dexterous manipulation\". In: CoRL. 2020."},{"key":"e_1_2_1_49_1","volume-title":"Locality-Aware Graph Neural Networks using Reachability Estimations","author":"Nishad Sunil","year":"2021","unstructured":"Sunil Nishad , Shubhangi Agarwal , Arnab Bhattacharya , and Sayan Ranu . \"GraphReach : Locality-Aware Graph Neural Networks using Reachability Estimations \". In : IJCAI. 2021 . SIGKDD Explorations Volume 23, Issue 2 21 Sunil Nishad, Shubhangi Agarwal, Arnab Bhattacharya, and Sayan Ranu. \"GraphReach: Locality-Aware Graph Neural Networks using Reachability Estimations\". In: IJCAI. 2021. SIGKDD Explorations Volume 23, Issue 2 21"},{"key":"e_1_2_1_50_1","volume-title":"TKDE","author":"Pan Zheyi","year":"2020","unstructured":"Zheyi Pan , Wentao Zhang , Yuxuan Liang , Weinan Zhang , Yong Yu , Junbo Zhang , and Yu Zheng . \" Spatio-Temporal Meta Learning for Urban Traffic Prediction\". In: TKDE ( 2020 ). Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang, and Yu Zheng. \"Spatio-Temporal Meta Learning for Urban Traffic Prediction\". In: TKDE (2020)."},{"key":"e_1_2_1_51_1","first-page":"55","article-title":"Excess risk bounds for multitask learning with trace norm regularization","author":"Pontil Massimiliano","year":"2013","unstructured":"Massimiliano Pontil and Andreas Maurer . \" Excess risk bounds for multitask learning with trace norm regularization \". In: COLT. 2013 , pp. 55 -- 76 . Massimiliano Pontil and Andreas Maurer. \"Excess risk bounds for multitask learning with trace norm regularization\". In: COLT. 2013, pp. 55--76.","journal-title":"COLT."},{"key":"e_1_2_1_52_1","volume-title":"ICLR.","author":"Ravi Sachin","year":"2017","unstructured":"Sachin Ravi and Hugo Larochelle . \" Optimization as a model for few-shot learning\". In: ICLR. 2017 . Sachin Ravi and Hugo Larochelle. \"Optimization as a model for few-shot learning\". In: ICLR. 2017."},{"key":"e_1_2_1_53_1","first-page":"61","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini. \"The graph neural network model\". In: IEEE transactions on neural networks 20.1","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. \"The graph neural network model\". In: IEEE transactions on neural networks 20.1 ( 2008 ), pp. 61 -- 80 . Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. \"The graph neural network model\". In: IEEE transactions on neural networks 20.1 (2008), pp. 61--80."},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.08.010"},{"key":"e_1_2_1_56_1","volume-title":"arXiv preprint arXiv:1811.05868","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur , Maximilian Mumme , Aleksandar Bojchevski , and Stephan G\u00a8unnemann . \" Pitfalls of graph neural network evaluation\". In: arXiv preprint arXiv:1811.05868 ( 2018 ). Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00a8unnemann. \"Pitfalls of graph neural network evaluation\". In: arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_2_1_57_1","first-page":"688","volume-title":"A deep learning approach to antibiotic discovery","author":"Stokes Jonathan M","year":"2020","unstructured":"Jonathan M Stokes , Kevin Yang , Kyle Swanson , Wengong Jin , Andres Cubillos-Ruiz , \" A deep learning approach to antibiotic discovery \". In : Cell ( 2020 ), pp. 688 -- 702 . Jonathan M Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, et al. \"A deep learning approach to antibiotic discovery\". In: Cell (2020), pp. 688--702."},{"key":"e_1_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367722"},{"key":"e_1_2_1_59_1","volume-title":"arXiv preprint arXiv:2002.11684","author":"Tripuraneni Nilesh","year":"2020","unstructured":"Nilesh Tripuraneni , Chi Jin , and Michael I Jordan . \" Provable meta-learning of linear representations\". In: arXiv preprint arXiv:2002.11684 ( 2020 ). Nilesh Tripuraneni, Chi Jin, and Michael I Jordan. \"Provable meta-learning of linear representations\". In: arXiv preprint arXiv:2002.11684 (2020)."},{"key":"e_1_2_1_60_1","first-page":"33","article-title":"On the Theory of Transfer Learning: The Importance of Task Diversity","author":"Tripuraneni Nilesh","year":"2020","unstructured":"Nilesh Tripuraneni , Michael Jordan , and Chi Jin . \" On the Theory of Transfer Learning: The Importance of Task Diversity \". In: NeurIPS 33 ( 2020 ). Nilesh Tripuraneni, Michael Jordan, and Chi Jin. \"On the Theory of Transfer Learning: The Importance of Task Diversity\". In: NeurIPS 33 (2020).","journal-title":"NeurIPS"},{"key":"e_1_2_1_61_1","volume-title":"ICLR","author":"\u00b4c Petar","year":"2018","unstructured":"Petar Veli?ckovi \u00b4c , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . \" Graph attention networks\". In: ICLR ( 2018 ). Petar Veli?ckovi\u00b4c, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. \"Graph attention networks\". In: ICLR (2018)."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411923"},{"key":"e_1_2_1_63_1","volume-title":"Revisiting nearest-neighbor classification for few-shot learning\". In: arXiv:1911.04623","author":"Wang Yan","year":"2019","unstructured":"Yan Wang , Wei-Lun Chao , Kilian Q Weinberger , and Laurens van der Maaten . \"Simpleshot : Revisiting nearest-neighbor classification for few-shot learning\". In: arXiv:1911.04623 ( 2019 ). Yan Wang, Wei-Lun Chao, Kilian Q Weinberger, and Laurens van der Maaten. \"Simpleshot: Revisiting nearest-neighbor classification for few-shot learning\". In: arXiv:1911.04623 (2019)."},{"key":"e_1_2_1_64_1","volume-title":"arXiv:2105.06725","author":"Wen Zhihao","year":"2021","unstructured":"Zhihao Wen , Yuan Fang , and Zemin Liu . \" Meta-Inductive Node Classification across Graphs\". In: arXiv:2105.06725 ( 2021 ). Zhihao Wen, Yuan Fang, and Zemin Liu. \"Meta-Inductive Node Classification across Graphs\". In: arXiv:2105.06725 (2021)."},{"key":"e_1_2_1_65_1","volume-title":"Kayla de la Haye, and Milind Tambe. \"Optimizing Network Structure for Preventative Health\". In: AAMAS.","author":"Wilder Bryan","year":"2018","unstructured":"Bryan Wilder , Han Ching Ou , Kayla de la Haye, and Milind Tambe. \"Optimizing Network Structure for Preventative Health\". In: AAMAS. 2018 . Bryan Wilder, Han Ching Ou, Kayla de la Haye, and Milind Tambe. \"Optimizing Network Structure for Preventative Health\". In: AAMAS. 2018."},{"key":"e_1_2_1_66_1","first-page":"1184","volume-title":"IEEE transactions on medical imaging 39.4","author":"Phang Jason","year":"2019","unstructured":"NanWu, Jason Phang , Jungkyu Park , Yiqiu Shen , \" Deep neural networks improve radiologists' performance in breast cancer screening \". In: IEEE transactions on medical imaging 39.4 ( 2019 ), pp. 1184 -- 1194 . NanWu, Jason Phang, Jungkyu Park, Yiqiu Shen, et al. \"Deep neural networks improve radiologists' performance in breast cancer screening\". In: IEEE transactions on medical imaging 39.4 (2019), pp. 1184--1194."},{"key":"e_1_2_1_67_1","volume-title":"IEEE transactions on neural networks and learning systems","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu , Shirui Pan , Fengwen Chen , Guodong Long , Chengqi Zhang , and S Yu Philip . \" A comprehensive survey on graph neural networks\". In: IEEE transactions on neural networks and learning systems ( 2020 ). Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. \"A comprehensive survey on graph neural networks\". In: IEEE transactions on neural networks and learning systems (2020)."},{"key":"e_1_2_1_68_1","volume-title":"ICLR","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . \" How powerful are graph neural networks?\" In : ICLR ( 2018 ). Keyulu Xu,Weihua Hu, Jure Leskovec, and Stefanie Jegelka. \"How powerful are graph neural networks?\" In: ICLR (2018)."},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_2_1_70_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6142"},{"key":"e_1_2_1_71_1","first-page":"7134","article-title":"Position-aware Graph Neural Networks","author":"You Jiaxuan","year":"2019","unstructured":"Jiaxuan You , Rex Ying , and Jure Leskovec . \" Position-aware Graph Neural Networks \". In: ICML. 2019 , pp. 7134 -- 7143 . Jiaxuan You, Rex Ying, and Jure Leskovec. \"Position-aware Graph Neural Networks\". In: ICML. 2019, pp. 7134--7143.","journal-title":"ICML."},{"key":"e_1_2_1_72_1","volume-title":"Gated attention networks for learning on large and spatiotemporal graphs","author":"Zhang Jiani","year":"2018","unstructured":"Jiani Zhang , Xingjian Shi , Junyuan Xie , Hao Ma , Irwin King , and Dit-Yan Yeung . \"Gaan : Gated attention networks for learning on large and spatiotemporal graphs \". In : UAI ( 2018 ). Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung. \"Gaan: Gated attention networks for learning on large and spatiotemporal graphs\". In: UAI (2018)."},{"key":"e_1_2_1_73_1","volume-title":"NeurIPS","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen . \" Link prediction based on graph neural networks\". In: NeurIPS ( 2018 ). Muhan Zhang and Yixin Chen. \"Link prediction based on graph neural networks\". In: NeurIPS (2018)."},{"key":"e_1_2_1_74_1","volume-title":"Meta-learning for clinical risk prediction with limited patient electronic health records","author":"Zhang Xi Sheryl","year":"2019","unstructured":"Xi Sheryl Zhang , Fengyi Tang , Hiroko H Dodge , Jiayu Zhou , and Fei Wang . \"Metapred : Meta-learning for clinical risk prediction with limited patient electronic health records \". In : KDD. 2019 . Xi Sheryl Zhang, Fengyi Tang, Hiroko H Dodge, Jiayu Zhou, and Fei Wang. \"Metapred: Meta-learning for clinical risk prediction with limited patient electronic health records\". In: KDD. 2019."},{"key":"e_1_2_1_75_1","first-page":"686","article-title":"Fast network alignment via graph meta-learning","author":"Zhou Fan","year":"2020","unstructured":"Fan Zhou , Chengtai Cao , Goce Trajcevski , Kunpeng Zhang , Ting Zhong , and Ji Geng . \" Fast network alignment via graph meta-learning \". In: INFOCOM. 2020 , pp. 686 -- 695 . Fan Zhou, Chengtai Cao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong, and Ji Geng. \"Fast network alignment via graph meta-learning\". In: INFOCOM. 2020, pp. 686--695.","journal-title":"INFOCOM."},{"key":"e_1_2_1_76_1","first-page":"2357","volume-title":"On Few-Shot Node Classification in Graph Meta-Learning","author":"Zhou Fan","year":"2019","unstructured":"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 , pp. 2357 -- 2360 . 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, pp. 2357--2360."},{"key":"e_1_2_1_77_1","volume-title":"A review of methods and applications\". In: arXiv preprint arXiv:1812.08434","author":"Zhou Jie","year":"2018","unstructured":"Jie Zhou , Ganqu Cui , Zhengyan Zhang , Cheng Yang , Zhiyuan Liu , LifengWang, Changcheng Li , and Maosong Sun . \" Graph neural networks : A review of methods and applications\". In: arXiv preprint arXiv:1812.08434 ( 2018 ). Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, LifengWang, Changcheng Li, and Maosong Sun. \"Graph neural networks: A review of methods and applications\". 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