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Node Classification

543 papers with code • 114 benchmarks • 53 datasets

The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.

Node classification models aim to predict non-existing node properties (known as the target propert) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

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Graph Attention Networks

PetarV-/GAT • • ICLR 2018

We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.

78
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Semi-Supervised Classification with Graph Convolutional Networks

dmlc/dgl • • 9 Sep 2016

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.

39
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Modeling Relational Data with Graph Convolutional Networks

tkipf/relational-gcn • • 17 Mar 2017

We demonstrate the effectiveness of R-GCNs as a stand-alone model for entity classification.

23
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Revisiting Semi-Supervised Learning with Graph Embeddings

tkipf/gcn • • 29 Mar 2016

We present a semi-supervised learning framework based on graph embeddings.

20
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How Powerful are Graph Neural Networks?

weihua916/powerful-gnns • • ICLR 2019

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

18
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node2vec: Scalable Feature Learning for Networks

dmlc/dgl • • 3 Jul 2016

Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

17
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Neural Message Passing for Quantum Chemistry

brain-research/mpnn • • ICML 2017

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science.

17
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DeepWalk: Online Learning of Social Representations

PaddlePaddle/PaddleRec • • 26 Mar 2014

We present DeepWalk, a novel approach for learning latent representations of vertices in a network.

14
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Benchmarking Graph Neural Networks

graphdeeplearning/benchmarking-gnns • • 2 Mar 2020

In the last few years, graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs.

14
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Hierarchical Graph Representation Learning with Differentiable Pooling

dmlc/dgl • • NeurIPS 2018

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

13
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