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 801–850 of 1860 papers

TitleStatusHype
A Complex Network based Graph Embedding Method for Link Prediction—0
Generating Human Understandable Explanations for Node Embeddings—0
A Graph Data Augmentation Strategy with Entropy Preservation—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
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Graph Neural Networks with Feature and Structure Aware Random Walk—0
Hyperbolic Heterogeneous Graph Attention Networks—0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification—0
Convolutional Networks on Enhanced Message-Passing Graph Improve Semi-Supervised Classification with Few Labels—0
Graph Neural Tangent Kernel: Convergence on Large Graphs—0
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks—0
Robust Graph Data Learning via Latent Graph Convolutional Representation—0
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling—0
hpGAT: High-order Proximity Informed Graph Attention Network—0
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—0
Attributed Graph Learning with 2-D Graph Convolution—0
Hub-aware Random Walk Graph Embedding Methods for Classification—0
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks—0
Graph Polynomial Convolution Models for Node Classification of Non-Homophilous Graphs—0
A Graph Convolutional Network Composition Framework for Semi-supervised Classification—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Graph Positional Encoding via Random Feature Propagation—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph—0
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels—0
How Frequency Effect Graph Neural Networks—0
GANN: Graph Alignment Neural Network for Semi-Supervised Learning—0
Contrastive Disentangled Learning on Graph for Node Classification—0
Beyond Homophily with Graph Echo State Networks—0
Optimal Propagation for Graph Neural Networks—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
Game-theoretic Counterfactual Explanation for Graph Neural Networks—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank—0
How Particle System Theory Enhances Hypergraph Message Passing—0
Training-Free Message Passing for Learning on Hypergraphs—0
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs—0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
Knowledge Probing for Graph Representation Learning—0
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs—0
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE—0
Graph Structural Aggregation for Explainable Learning—0
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck—0
Graph Structure Refinement with Energy-based Contrastive Learning—0
AKE-GNN: Effective Graph Learning with Adaptive Knowledge Exchange—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
Show:102550
← PrevPage 17 of 38Next →
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
6CleoraAccuracy86.8—Unverified
7NodeNetAccuracy86.8—Unverified
8MMAAccuracy85.8—Unverified
9GResNet(GAT)Accuracy85.5—Unverified
10TransGNN1:1 Accuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5—Unverified
2LDS-GNNAccuracy75—Unverified
3CPF-tra-APPNPAccuracy74.6—Unverified
4G3NNAccuracy74.5—Unverified
5GEMAccuracy74.2—Unverified
6GGCMAccuracy74.2—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
4Truncated KrylovAccuracy81.7—Unverified
5SuperGAT MXAccuracy81.7—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