Semi-Supervised Classification with Graph Convolutional Networks

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International Conference on Learning Representa...(2016)

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TL;DR

A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.

Abstract

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.

Authors

M. Welling

17 papers

Thomas Kipf

11 papers

References39 items

node2vec: Scalable Feature Learning for Networks

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Learning Convolutional Neural Networks for Graphs

Revisiting Semi-Supervised Learning with Graph Embeddings

Deep Residual Learning for Image Recognition

Semi-Supervised Classification with Graph Convolutional Networks

Published in

International Conference on Learning Representa...(2016)

External Links:

Generate Graph

TL;DR

Abstract

Authors

M. Welling

17 papers

Thomas Kipf

11 papers

References39 items

node2vec: Scalable Feature Learning for Networks

Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

Learning Convolutional Neural Networks for Graphs

Revisiting Semi-Supervised Learning with Graph Embeddings

Deep Residual Learning for Image Recognition

Gated Graph Sequence Neural Networks

Diffusion-Convolutional Neural Networks

Convolutional Networks on Graphs for Learning Molecular Fingerprints

LINE: Large-scale Information Network Embedding

Adam: A Method for Stochastic Optimization

DeepWalk: online learning of social representations

Spectral Networks and Locally Connected Networks on Graphs

Wavelet-regularized graph semi-supervised learning

Distributed Representations of Words and Phrases and their Compositionality

The conference paper

The Weisfeiler-Lehman Method and Graph Isomorphism Testing

Toward an Architecture for Never-Ending Language Learning

Understanding the difficulty of training deep feedforward neural networks

Wavelets on Graphs via Spectral Graph Theory

Collective Classification in Network Data

Deep learning via semi-supervised embedding

On Modularity Clustering

Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples

A new model for learning in graph domains

Learning with Local and Global Consistency

Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions

Link-Based Classification

Transductive Inference for Text Classification using Support Vector Machines

An Information Flow Model for Conflict and Fission in Small Groups

Under review as a conference paper at ICLR 2020 many domain adaptation methods

Published as a conference paper at ICLR 2018 S IMULATING A CTION D YNAMICS WITH N EURAL P ROCESS N ETWORKS

TensorFlow: Large-scale machine learning on heterogeneous systems

Dropout: a simple way to prevent neural networks from overfitting

Semi-Supervised Learning with Spectral Graph Wavelets

The Graph Neural Network Model

Visualizing Data using t-SNE

Collective Classi!cation in Network Data

A reduction of a graph to a canonical form and an algebra arising during this reduction

A reduction of a graph to a canonical form and an algebra arising during this reduction. Nauchno-Technicheskaya Informatsia

Field of Study

MathematicsComputer Science

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

International Conference on Learning Representations

Type

conference

URL

https://iclr.cc/

Alternate Names

Int Conf Learn Represent
ICLR