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Node Classification

783 papers with code • 122 benchmarks • 69 datasets

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) 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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Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks

dglai/dgl-0.5-benchmark • • 3 Sep 2019

Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs.

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Graph Random Neural Network for Semi-Supervised Learning on Graphs

Grand20/grand • • 22 May 2020

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored.

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Combining Label Propagation and Simple Models Out-performs Graph Neural Networks

CUAI/CorrectAndSmooth • • ICLR 2021

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.

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Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

google-research/google-research • • KDD 2019

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

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Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsters

safe-graph/DGFraud • • 19 Aug 2020

Finally, the selected neighbors across different relations are aggregated together.

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OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs

snap-stanford/ogb • • 17 Mar 2021

Enabling effective and efficient machine learning (ML) over large-scale graph data (e. g., graphs with billions of edges) can have a great impact on both industrial and scientific applications.

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struc2vec: Learning Node Representations from Structural Identity

leoribeiro/struc2vec • 11 Apr 2017

Implementation and experiments of graph embedding algorithms. deep walk, LINE(Large-scale Information Network Embedding), node2vec, SDNE(Structural Deep Network Embedding), struc2vec

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GraphGAN: Graph Representation Learning with Generative Adversarial Nets

hwwang55/GraphGAN • • 22 Nov 2017

The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space.

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SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

rusty1s/pytorch_geometric • • CVPR 2018

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.

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Predict then Propagate: Graph Neural Networks meet Personalized PageRank

klicperajo/ppnp • • ICLR 2019

We utilize this propagation procedure to construct a simple model, personalized propagation of neural predictions (PPNP), and its fast approximation, APPNP.

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