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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.
“Online meta-learning”
Chelsea Finn, Aravind Rajeswaran, Sham Kakade and Sergey Levine · 1930
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“Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook”, 1987
J“”urgen Schmidhuber · 1987
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“A model of inductive bias learning”
Jonathan Baxter · 2000
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“Maximizing the spread of influence through a social network”
David Kempe, Jon Kleinberg and “’Eva Tardos · 2003
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“Protein function prediction via graph kernels”
Karsten Borgwardt, Cheng Ong, Stefan Sch“”onauer, SVN Vishwanathan, Alex Smola and Hans-Peter Kriegel · 2005
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“The graph neural network model”
Franco Scarselli, Marco Gori, Ah Tsoi, Markus Hagenbuchner and Gabriele Monfardini · 2008
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“Arnetminer: extraction and mining of academic social networks”
Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang and Zhong Su · 2008
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“Excess risk bounds for multitask learning with trace norm regularization”
Massimiliano Pontil and Andreas Maurer · 2013
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“Deep graph kernels”
Pinar Yanardag and SVN Vishwanathan · 2015
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“Deep neural networks for learning graph representations”
Shaosheng Cao, Wei Lu and Qiongkai Xu · 2016
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“Variational graph auto-encoders”
Thomas Kipf and Max Welling · 2016
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“Gated graph sequence neural networks”
Yujia Li, Daniel Tarlow, Marc Brockschmidt and Richard Zemel · 2016
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“The benefit of multitask representation learning”
Andreas Maurer, Massimiliano Pontil and Bernardino Romera-Paredes · 2016
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“Learning combinatorial optimization algorithms over graphs”
Hanjun Dai, Elias Khalil, Yuyu Zhang, Bistra Dilkina and Le Song · 2017
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“Model-agnostic meta-learning for fast adaptation of deep networks”
Chelsea Finn, Pieter Abbeel and Sergey Levine · 2017
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“Inductive representation learning on large graphs”
Will Hamilton, Zhitao Ying and Jure Leskovec · 2017
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“Representation learning on graphs: Methods and applications”
William Hamilton, Rex Ying and Jure Leskovec · 2017
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“Semi-supervised classification with graph convolutional networks”
Thomas Kipf and Max Welling · 2017
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“Optimization as a model for few-shot learning”
Sachin Ravi and Hugo Larochelle · 2017
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“Combinatorial optimization with graph convolutional networks and guided tree search”
Zhuwen Li, Qifeng Chen and Vladlen Koltun · 2018
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“Noticeable network delay minimization via node upgrades”
Sourav Medya, Jithin Vachery, Sayan Ranu and Ambuj Singh · 2018
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“Pitfalls of graph neural network evaluation”
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski and Stephan G“”unnemann · 2018
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“The Vapnik–Chervonenkis dimension of graph and recursive neural networks”
Franco Scarselli, Ah Tsoi and Markus Hagenbuchner · 2018
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“Graph attention networks”
Petar Velickovi“’c, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio and Yoshua Bengio · 2018
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“Optimizing Network Structure for Preventative Health”
Bryan Wilder, Han Ou, Kayla de Haye and Milind Tambe · 2018
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“How powerful are graph neural networks?”
Keyulu Xu, Weihua Hu, Jure Leskovec and Stefanie Jegelka · 2018
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“Link prediction based on graph neural networks”
Muhan Zhang and Yixin Chen · 2018
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“Gaan: Gated attention networks for learning on large and spatiotemporal graphs”
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King and Dit-Yan Yeung · 2018
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“Graph neural networks: A review of methods and applications”
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li and Maosong Sun · 2018
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“SimGNN: A Neural Network Approach to Fast Graph Similarity Computation”
Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun and Wei Wang · 2019
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“Provable guarantees for gradient-based meta-learning”
Maria-Florina Balcan, Mikhail Khodak and Ameet Talwalkar · 2019
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“Meta-learning in neural networks: A survey”
Timothy Hospedales, Antreas Antoniou, Paul Micaelli and Amos Storkey · 2020
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“Graph meta learning via local subgraphs”
Kexin Huang and Marinka Zitnik · 2020
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“Node classification on graphs with few-shot novel labels via meta transformed network embedding”
Lin Lan, Pinghui Wang, Xuefeng Du, Kaikai Song, Jing Tao and Xiaohong Guan · 2020
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“Towards locality-aware meta-learning of tail node embeddings on networks”
Zemin Liu, Wentao Zhang, Yuan Fang, Xinming Zhang and Steven Hoi · 2020
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“Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification”
Ning Ma, Jiajun Bu, Jieyu Yang, Zhen Zhang, Chengwei Yao, Zhi Yu, Sheng Zhou and Xifeng Yan · 2020
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Avishek Bose, Ankit Jain, Piero Molino and William Hamilton · 2019
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“Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs”
Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen and Huajun Chen · 2019
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“Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting”
Zhiyong Cui, Kristian Henrickson, Ruimin Ke and Yinhai Wang · 2019
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“Learning-to-learn stochastic gradient descent with biased regularization”
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi and Massimiliano Pontil · 2019
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“Bert: Pre-training of deep bidirectional transformers for language understanding”
Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova · 2019
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“A fair comparison of graph neural networks for graph classification”
Federico Errica, Marco Podda, Davide Bacciu and Alessio Micheli · 2019
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“Exact combinatorial optimization with graph convolutional neural networks”
Maxime Gasse, Didier Ch“’etelat, Nicola Ferroni, Laurent Charlin and Andrea Lodi · 2019
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“GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs”
Sahil Manchanda, Akash Mittal, Anuj Dhawan, Sourav Medya, Sayan Ranu and Ambuj Singh · 2020
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“Deep dynamics models for learning dexterous manipulation”
Anusha Nagabandi, Kurt Konolige, Sergey Levine and Vikash Kumar · 2020
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“Spatio-Temporal Meta Learning for Urban Traffic Prediction”
Zheyi Pan, Wentao Zhang, Yuxuan Liang, Weinan Zhang, Yong Yu, Junbo Zhang and Yu Zheng · 2020
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“A deep learning approach to antibiotic discovery”
Jonathan Stokes, Kevin Yang, Kyle Swanson, Wengong Jin and Andres Cubillos-Ruiz · 2020
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“Provable meta-learning of linear representations”
Nilesh Tripuraneni, Chi Jin and Michael Jordan · 2020
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“On the Theory of Transfer Learning: The Importance of Task Diversity”
Nilesh Tripuraneni, Michael Jordan and Chi Jin · 2020
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“Graph Few-shot Learning with Attribute Matching”
Ning Wang, Minnan Luo, Kaize Ding, Lingling Zhang, Jundong Li and Qinghua Zheng · 2020
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“A comprehensive survey on graph neural networks”
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang and S Philip · 2020
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“Graph few-shot learning via knowledge transfer”
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla and Zhenhui Li · 2020
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“Fast network alignment via graph meta-learning”
Fan Zhou, Chengtai Cao, Goce Trajcevski, Kunpeng Zhang, Ting Zhong and Ji Geng · 2020
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“Combinatorial optimization and reasoning with graph neural networks”
Quentin Cappart, Didier Ch“’etelat, Elias Khalil, Andrea Lodi, Christopher Morris and Petar Velickovi“’c · 2021
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“Weakly-supervised Graph Meta-learning for Few-shot Node Classification”
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“Few-Shot Graph Learning for Molecular Property Prediction”
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“Structure-Enhanced Meta-Learning For Few-Shot Graph Classification”
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“Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph”
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