Semi-Supervised Classification with Graph Convolutional Networks

Thomas Kipf; Max Welling

doi:https://doi.org/10.48550/arxiv.1609.02907

Semi-Supervised Classification with Graph Convolutional Networks

2016 · arXiv (Cornell University) · 8,058 citations · 14 from inside this corpus

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

22 of 22 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Modeling Relational Data with Graph Convolutional Networks→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Heterogeneous Graph Attention Network→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
A survey on semi-supervised learning→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Convolutional 2D Knowledge Graph Embeddings→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Graph Convolutional Networks for Text Classification→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Graph convolutional networks: a comprehensive review→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Hypergraph Neural Networks→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Unsupervised Data Augmentation for Consistency Training→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
A survey on deep learning and its applications→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Heterogeneous Graph Neural Network→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Pre-trained models for natural language processing: A survey→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Heterogeneous Graph Transformer→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
DeepGCNs: Can GCNs Go As Deep As CNNs?→Semi-Supervised Classification with Graph Convolutional NetworksLegacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→The Graph Neural Network ModelLegacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…Legacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…Legacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→Transductive Inference for Text Classification using Support Vector MachinesLegacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→Toward an Architecture for Never-Ending Language LearningLegacy edge — source detail unavailable
Semi-Supervised Classification with Graph Convolutional Networks→A new model for learning in graph domainsLegacy edge — source detail unavailable
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…→Transductive Inference for Text Classification using Support Vector MachinesLegacy edge — source detail unavailable
The Graph Neural Network Model→A new model for learning in graph domainsLegacy edge — source detail unavailable
Modeling Relational Data with Graph Convolutional Networks→The Graph Neural Network ModelLegacy edge — source detail unavailable
Modeling Relational Data with Graph Convolutional Networks→Convolutional 2D Knowledge Graph EmbeddingsLegacy edge — source detail unavailable
Convolutional 2D Knowledge Graph Embeddings→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Convolutional 2D Knowledge Graph Embeddings→Modeling Relational Data with Graph Convolutional NetworksLegacy edge — source detail unavailable
Convolutional 2D Knowledge Graph Embeddings→Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…Legacy edge — source detail unavailable
Hypergraph Neural Networks→A new model for learning in graph domainsLegacy edge — source detail unavailable
Hypergraph Neural Networks→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Hypergraph Neural Networks→The Graph Neural Network ModelLegacy edge — source detail unavailable
Heterogeneous Graph Attention Network→A new model for learning in graph domainsLegacy edge — source detail unavailable
Heterogeneous Graph Attention Network→The Graph Neural Network ModelLegacy edge — source detail unavailable
Heterogeneous Graph Attention Network→Modeling Relational Data with Graph Convolutional NetworksLegacy edge — source detail unavailable
Heterogeneous Graph Attention Network→[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…Legacy edge — source detail unavailable
Heterogeneous Graph Attention Network→Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…Legacy edge — source detail unavailable
Graph Convolutional Networks for Text Classification→Deeper Insights Into Graph Convolutional Networks for Semi-Supervised LearningLegacy edge — source detail unavailable
Graph Convolutional Networks for Text Classification→Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…Legacy edge — source detail unavailable
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning→Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…Legacy edge — source detail unavailable
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning→Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…Legacy edge — source detail unavailable
Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…→The Graph Neural Network ModelLegacy edge — source detail unavailable
Heterogeneous Graph Neural Network→Modeling Relational Data with Graph Convolutional NetworksLegacy edge — source detail unavailable
A survey on semi-supervised learning→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
A survey on semi-supervised learning→Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…Legacy edge — source detail unavailable
A survey on semi-supervised learning→Transductive Inference for Text Classification using Support Vector MachinesLegacy edge — source detail unavailable
Graph convolutional networks: a comprehensive review→The Graph Neural Network ModelLegacy edge — source detail unavailable
Graph convolutional networks: a comprehensive review→Graph Convolutional Networks for Text ClassificationLegacy edge — source detail unavailable
Graph convolutional networks: a comprehensive review→Deeper Insights Into Graph Convolutional Networks for Semi-Supervised LearningLegacy edge — source detail unavailable
DeepGCNs: Can GCNs Go As Deep As CNNs?→Deeper Insights Into Graph Convolutional Networks for Semi-Supervised LearningLegacy edge — source detail unavailable
Heterogeneous Graph Transformer→Modeling Relational Data with Graph Convolutional NetworksLegacy edge — source detail unavailable
Heterogeneous Graph Transformer→Heterogeneous Graph Attention NetworkLegacy edge — source detail unavailable
Heterogeneous Graph Transformer→Heterogeneous Graph Neural NetworkLegacy edge — source detail unavailable
A survey on deep learning and its applications→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
[No title in the source record — DROPS (Schloss Dagstuhl – Leibniz Center for Informatics…	2015	50,396
Dropout: a simple way to prevent neural networks from overfitting	2014	34,236
The Graph Neural Network Model	2008	9,662
Convolutional Neural Networks On Graphs With Fast Localized Spectral Filtering (Netsci-X'…	2016	5,118
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Ex…	2006	3,266
Transductive Inference for Text Classification using Support Vector Machines	1999	2,721
Toward an Architecture for Never-Ending Language Learning	2010	1,996
A new model for learning in graph domains	2006	1,944

What cites it, inside the corpus

Paper	Year	Cited
Modeling Relational Data with Graph Convolutional Networks	2018	5,233
Heterogeneous Graph Attention Network	2019	2,999
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning	2018	2,652
A survey on semi-supervised learning	2019	2,636
Convolutional 2D Knowledge Graph Embeddings	2018	2,467
Graph Convolutional Networks for Text Classification	2019	2,000
Graph convolutional networks: a comprehensive review	2019	1,877
Hypergraph Neural Networks	2019	1,720
Unsupervised Data Augmentation for Consistency Training	2019	1,624
A survey on deep learning and its applications	2021	1,548
Heterogeneous Graph Neural Network	2019	1,532
Pre-trained models for natural language processing: A survey	2020	1,521
Heterogeneous Graph Transformer	2020	1,432
DeepGCNs: Can GCNs Go As Deep As CNNs?	2019	1,380

Links

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Topics

Advanced Graph Neural Networks	Computer Science
Domain Adaptation and Few-Shot Learning	Computer Science
Text and Document Classification Technologies	Computer Science

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complete

Nothing in this record contradicts itself and no field we check is missing.

supports2 author record(s) attached.
supports22 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

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