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Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes.
Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Inferring networks of substitutable and complementary products
Julian McAuley, Rahul Pandey, and Jure Leskovec · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William Cohen, and Ruslan Salakhudinov · 2016
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Inductive Representation Learning on Large Graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 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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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Graph Attention Networks
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2018
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Few-shot classification on graphs with structural regularized gcns
Shengzhong Zhang, Ziang Zhou, Zengfeng Huang, and Zhongyu Wei · 2018
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A simple neural attentive meta-learner
Nikhil Mishra, Mostafa Rohaninejad, Xi Chen, and Pieter Abbeel · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Learning to compare: relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
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Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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A baseline for few-shot image classification
Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2019
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Learning to propagate for graph meta-learning
Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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Meta-gnn: On few-shot node classification in graph meta-learning
Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Pires, Zhaohan Guo, Mohammad Azar, et al · 2020
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Multi-scale contrastive siamese networks for self-supervised graph representation learning
Ming Jin, Yizhen Zheng, Yuan-Fang Li, Chen Gong, Chuan Zhou, and Shirui Pan · 2021
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Reform: Error-aware few-shot knowledge graph completion
Song Wang, Xiao Huang, Chen Chen, Liang Wu, and Jundong Li · 2021
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Deep graph library: A graph-centric, highly-performant package for graph neural networks
Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang · 2019
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Graph prototypical networks for few-shot learning on attributed networks
Kaize Ding, Jianling Wang, Jundong Li, Kai Shu, Chenghao Liu, and Huan Liu · 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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Rethinking few-shot image classification: a good embedding is all you need?
Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola · 2020
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Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
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Relative and absolute location embedding for few-shot node classification on graph
Zemin Liu, Yuan Fang, Chenghao Liu, and Steven CH Hoi · 2021
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Self-supervised graph-level representation learning with local and global structure
Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang · 2021
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Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
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An empirical study of graph contrastive learning
Yanqiao Zhu, Yichen Xu, Qiang Liu, and Shu Wu · 2021
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Few-shot learning on graphs: A survey
Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V Chawla, and Huan Liu · 2022
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Simple unsupervised graph representation learning
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Graph few-shot class-incremental learning
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Few-shot node classification on attributed networks with graph meta-learning
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Eliciting structural and semantic global knowledge in unsupervised graph contrastive learning
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