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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1351–1400 of 1860 papers

TitleStatusHype
Graph-Graph Similarity Network—0
Label Efficient Regularization and Propagation for Graph Node Classification—0
Graph Inference Learning for Semi-supervised Classification—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Graph Information Matters: Understanding Graph Filters from Interaction Probability—0
GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs—0
Graph Joint Attention Networks—0
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Graph Masked Language Models—0
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks—0
Graph Mining under Data scarcity—0
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction—0
Graph Neural Aggregation-diffusion with Metastability—0
Graph Neural Networks at a Fraction—0
Graph Neural Networks for Binary Programming—0
Graph Neural Networks with a Distribution of Parametrized Graphs—0
Graph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise—0
Graph Neural Networks with Feature and Structure Aware Random Walk—0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification—0
Graph Neural Reaction Diffusion Models—0
Graph Neural Tangent Kernel: Convergence on Large Graphs—0
Robust Graph Data Learning via Latent Graph Convolutional Representation—0
Graph Ordering: Towards the Optimal by Learning—0
Graph Partner Neural Networks for Semi-Supervised Learning on Graphs—0
Graph Pointer Neural Networks—0
Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs—0
Graph Pooling by Edge Cut—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Positional Encoding via Random Feature Propagation—0
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy—0
Graph Sanitation with Application to Node Classification—0
Graph Semi-Supervised Learning for Point Classification on Data Manifolds—0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification—0
Graph Structural Aggregation for Explainable Learning—0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks—0
Graph Structure Refinement with Energy-based Contrastive Learning—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
Graph Transformer GANs for Graph-Constrained House Generation—0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Graph Transformers without Positional Encodings—0
Graph U-Net—0
Graph View-Consistent Learning Network—0
GraphViz2Vec: A Structure-aware Feature Generation Model to Improve Classification in GNNs—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks—0
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning—0
GUAP: Graph Universal Attack Through Adversarial Patching—0
Fast Haar Transforms for Graph Neural Networks—0
Haar Wavelet Feature Compression for Quantized Graph Convolutional Networks—0
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All datasetsCiteseerPubmedCoraCiteSeer with Public Split: fixed 20 nodes per classPubMed with Public Split: fixed 20 nodes per classCora with Public Split: fixed 20 nodes per classPPICoauthor CSCora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09—Unverified
2SplineCNNAccuracy79.2—Unverified
3PathNetAccuracy (%)77.98—Unverified
43ferenceAccuracy76.33—Unverified
5MMAAccuracy76.3—Unverified
6PPNPAccuracy75.83—Unverified
7CoLinkDistAccuracy75.79—Unverified
8CoLinkDistMLPAccuracy75.77—Unverified
9APPNPAccuracy75.73—Unverified
10CleoraAccuracy75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21—Unverified
2CoLinkDistAccuracy89.58—Unverified
3CoLinkDistMLPAccuracy89.53—Unverified
4PathNetAccuracy (%)88.92—Unverified
53ferenceAccuracy88.9—Unverified
6SplineCNNAccuracy88.88—Unverified
7LinkDistAccuracy88.86—Unverified
8LinkDistMLPAccuracy88.79—Unverified
9PairEF188.57—Unverified
10GCN + MixupAccuracy87.9—Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24—Unverified
2CoLinkDistAccuracy87.89—Unverified
33ferenceAccuracy87.78—Unverified
4LinkDistMLPAccuracy87.58—Unverified
5CoLinkDistMLPAccuracy87.54—Unverified
6NodeNetAccuracy86.8—Unverified
7CleoraAccuracy86.8—Unverified
8MMAAccuracy85.8—Unverified
9GResNet(GAT)Accuracy85.5—Unverified
10DifNetAccuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5—Unverified
2LDS-GNNAccuracy75—Unverified
3CPF-tra-APPNPAccuracy74.6—Unverified
4G3NNAccuracy74.5—Unverified
5GGCMAccuracy74.2—Unverified
6GEMAccuracy74.2—Unverified
7Truncated KrylovAccuracy73.86—Unverified
8SSGCAccuracy73.6—Unverified
9OKDEEMAccuracy73.53—Unverified
10GCNIIAccuracy73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4—Unverified
2CPF-tra-GCNIIAccuracy83.2—Unverified
3DSGCNAccuracy81.9—Unverified
4SuperGAT MXAccuracy81.7—Unverified
5Truncated KrylovAccuracy81.7—Unverified
6G-APPNPAccuracy80.95—Unverified
7GGCMAccuracy80.8—Unverified
8GCN(predicted-targets)Accuracy80.42—Unverified
9SSGCAccuracy80.4—Unverified
10GCNIIAccuracy80.2—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9—Unverified
2GCN-TVAccuracy86.3—Unverified
3GCNIIAccuracy85.5—Unverified
4CPF-ind-APPNPAccuracy85.3—Unverified
5AIR-GCNAccuracy84.7—Unverified
6H-GCNAccuracy84.5—Unverified
7G-APPNPAccuracy84.31—Unverified
8SuperGAT MXAccuracy84.3—Unverified
9DSGCNAccuracy84.2—Unverified
10LDS-GNNAccuracy84.1—Unverified