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 1251–1300 of 1860 papers

TitleStatusHype
Exploring Graph Neural Networks for Indian Legal Judgment Prediction—0
Pre-Trained Models for Heterogeneous Information Networks—0
Exploring the Potential of Large Language Models in Graph Generation—0
Exploring the Potential of Large Language Models for Heterophilic Graphs—0
Exponential Family Graph Embeddings—0
Extracting Shopping Interest-Related Product Types from the Web—0
FairGAT: Fairness-aware Graph Attention Networks—0
Fair Graph Neural Network with Supervised Contrastive Regularization—0
Fairness-Aware Graph Filter Design—0
Fairness-Aware Node Representation Learning—0
Fairness-aware Optimal Graph Filter Design—0
Fair Node Representation Learning via Adaptive Data Augmentation—0
FairNorm: Fair and Fast Graph Neural Network Training—0
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification—0
Fast and Effective GNN Training with Linearized Random Spanning Trees—0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation—0
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
Fast online node labeling with graph subsampling—0
FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS—0
Feature Distribution on Graph Topology Mediates the Effect of Graph Convolution: Homophily Perspective—0
Federated Learning over Coupled Graphs—0
Federated Learning with Limited Node Labels—0
FedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks—0
FedGT: Federated Node Classification with Scalable Graph Transformer—0
FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs—0
Few-shot Classification on Graphs with Structural Regularized GCNs—0
FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths—0
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Flexible infinite-width graph convolutional networks and the importance of representation learning—0
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation—0
FMGNN: Fused Manifold Graph Neural Network—0
FMP: Toward Fair Graph Message Passing against Topology Bias—0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification—0
Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey—0
Framelet Message Passing—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks—0
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck—0
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs—0
Game-theoretic Counterfactual Explanation for Graph Neural Networks—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
GANN: Graph Alignment Neural Network for Semi-Supervised Learning—0
GCExplainer: Human-in-the-Loop Concept-based Explanations for Graph Neural Networks—0
GCN-SE: Attention as Explainability for Node Classification in Dynamic Graphs—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
6CleoraAccuracy86.8—Unverified
7NodeNetAccuracy86.8—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