SOTAVerified

Node Classification

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 )

Papers

Showing 18011850 of 1860 papers

TitleStatusHype
GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal GraphsCode0
Graph Partition Neural Networks for Semi-Supervised ClassificationCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
MILE: A Multi-Level Framework for Scalable Graph EmbeddingCode0
N-GCN: Multi-scale Graph Convolution for Semi-supervised Node ClassificationCode0
Learning to Make Predictions on Graphs with AutoencodersCode0
Semi-Supervised Learning on Graphs Based on Local Label Distributions0
FastGCN: Fast Learning with Graph Convolutional Networks via Importance SamplingCode1
Deeper Insights into Graph Convolutional Networks for Semi-Supervised LearningCode0
GraphGAN: Generating Graphs via Random Walks0
Fast Node Embeddings: Learning Ego-Centric Representations0
Network of Graph Convolutional Networks Trained on Random Walks0
SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline KernelsCode3
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
Adversarial Network Embedding0
Residual Gated Graph ConvNetsCode0
Motif-based Convolutional Neural Network on GraphsCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
RDF2Vec: RDF Graph Embeddings and Their ApplicationsCode1
Graph Attention NetworksCode1
Watch Your Step: Learning Node Embeddings via Graph AttentionCode0
Sparse Diffusion-Convolutional Neural Networks0
Graph Embedding with Rich Information through Heterogeneous Network0
Supervised Q-walk for Learning Vector Representation of Nodes in Networks0
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications0
Network Vector: Distributed Representations of Networks with Global Context0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
Graph Convolution: A High-Order and Adaptive Approach0
Inductive Representation Learning on Large GraphsCode1
Attributed Network Embedding for Learning in a Dynamic Environment0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Active Learning for Graph EmbeddingCode0
struc2vec: Learning Node Representations from Structural IdentityCode0
Neural Message Passing for Quantum ChemistryCode1
Modeling Relational Data with Graph Convolutional NetworksCode1
Learning Deep Matrix Representations0
Distributed Representation of Subgraphs0
Geometric deep learning on graphs and manifolds using mixture model CNNsCode0
From Node Embedding To Community EmbeddingCode0
Dynamic Stacked Generalization for Node Classification on Networks0
A General Framework for Content-enhanced Network Representation Learning0
Semi-Supervised Classification with Graph Convolutional NetworksCode1
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks0
node2vec: Scalable Feature Learning for NetworksCode1
Convolutional Neural Networks on Graphs with Fast Localized Spectral FilteringCode0
Revisiting Semi-Supervised Learning with Graph EmbeddingsCode1
Gated Graph Sequence Neural NetworksCode1
Diffusion-Convolutional Neural NetworksCode0
GraRep: Learning Graph Representations with Global Structural InformationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09Unverified
2SplineCNNAccuracy79.2Unverified
3PathNetAccuracy (%)77.98Unverified
43ferenceAccuracy76.33Unverified
5MMAAccuracy76.3Unverified
6PPNPAccuracy75.83Unverified
7CoLinkDistAccuracy75.79Unverified
8CoLinkDistMLPAccuracy75.77Unverified
9APPNPAccuracy75.73Unverified
10CleoraAccuracy75.7Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21Unverified
2CoLinkDistAccuracy89.58Unverified
3CoLinkDistMLPAccuracy89.53Unverified
4PathNetAccuracy (%)88.92Unverified
53ferenceAccuracy88.9Unverified
6SplineCNNAccuracy88.88Unverified
7LinkDistAccuracy88.86Unverified
8LinkDistMLPAccuracy88.79Unverified
9PairEF188.57Unverified
10GCN + MixupAccuracy87.9Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24Unverified
2CoLinkDistAccuracy87.89Unverified
33ferenceAccuracy87.78Unverified
4LinkDistMLPAccuracy87.58Unverified
5CoLinkDistMLPAccuracy87.54Unverified
6NodeNetAccuracy86.8Unverified
7CleoraAccuracy86.8Unverified
8MMAAccuracy85.8Unverified
9GResNet(GAT)Accuracy85.5Unverified
10TransGNN1:1 Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GEMAccuracy74.2Unverified
6GGCMAccuracy74.2Unverified
7Truncated KrylovAccuracy73.86Unverified
8SSGCAccuracy73.6Unverified
9OKDEEMAccuracy73.53Unverified
10GCNIIAccuracy73.4Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4Unverified
2CPF-tra-GCNIIAccuracy83.2Unverified
3DSGCNAccuracy81.9Unverified
4SuperGAT MXAccuracy81.7Unverified
5Truncated KrylovAccuracy81.7Unverified
6G-APPNPAccuracy80.95Unverified
7GGCMAccuracy80.8Unverified
8GCN(predicted-targets)Accuracy80.42Unverified
9SSGCAccuracy80.4Unverified
10GCNIIAccuracy80.2Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9Unverified
2GCN-TVAccuracy86.3Unverified
3GCNIIAccuracy85.5Unverified
4CPF-ind-APPNPAccuracy85.3Unverified
5AIR-GCNAccuracy84.7Unverified
6H-GCNAccuracy84.5Unverified
7G-APPNPAccuracy84.31Unverified
8SuperGAT MXAccuracy84.3Unverified
9DSGCNAccuracy84.2Unverified
10LDS-GNNAccuracy84.1Unverified