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Prevailing methods for graphs require abundant label and edge information for learning.
The distance-weighted k-nearest-neighbor rule
Sahibsingh A Dudani · 1976
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Semi-supervised learning using gaussian fields and harmonic functions
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Dense subgraph extraction with application to community detection
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Pathway discovery in metabolic networks by subgraph extraction
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Graph kernels for object category prediction in task-dependent robot grasping
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Jason Weston, Sumit Chopra, and Antoine Bordes · 2014
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Image-based recommendations on styles and substitutes
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Combining satellite imagery and machine learning to predict poverty
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Variational graph auto-encoders
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Meta-learning with memory-augmented neural networks
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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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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. 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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Weisfeiler-lehman neural machine for link prediction
Muhan Zhang and Yixin Chen · 2017
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Predicting multicellular function through multi-layer tissue networks
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Deep graph infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2019
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Learning to learn how to learn: Self-adaptive visual navigation using meta-learning
Mitchell Wortsman, Kiana Ehsani, Mohammad Rastegari, Ali Farhadi, and Roozbeh Mottaghi · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
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How powerful are graph neural networks?
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Gnnexplainer: Generating explanations for graph neural networks
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Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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On first-order meta-learning algorithms
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Learning to compare: Relation network for few-shot learning
F. Sung, Y. Yang, L. Zhang, T. Xiang, P. H. S. Torr, and T. M. Hospedales · 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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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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Evolution of resilience in protein interactomes across the tree of life
Marinka Zitnik, Marcus W Feldman, Jure Leskovec, et al · 2019
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Subgraph neural networks
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The scop database in 2020: expanded classification of representative family and superfamily domains of known protein structures
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Few-shot learning on graphs via super-classes based on graph spectral measures
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Heterogeneous graph transformer
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A reference map of the human binary protein interactome
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Inductive relation prediction by subgraph reasoning
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Unifying graph convolutional neural networks and label propagation
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Graph few-shot learning via knowledge transfer
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Fast network alignment via graph meta-learning
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