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

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Showing 1651–1700 of 1860 papers

TitleStatusHype
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning—0
Predicting the structure of dynamic graphs—0
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs—0
Vertically Federated Graph Neural Network for Privacy-Preserving Node Classification—0
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective—0
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs—0
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks—0
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
Quantifying Challenges in the Application of Graph Representation Learning—0
Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations—0
Quantum-based subgraph convolutional neural networks—0
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs—0
QUINT: Node embedding using network hashing—0
RECS: Robust Graph Embedding Using Connection Subgraphs—0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models—0
Refined Edge Usage of Graph Neural Networks for Edge Prediction—0
REFINE: Random RangE FInder for Network Embedding—0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks—0
ReGrAt: Regularization in Graphs using Attention to handle class imbalance—0
Enabling Homogeneous GNNs to Handle Heterogeneous Graphs via Relation Embedding—0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
Relaxing Graph Transformers for Adversarial Attacks—0
Renormalized Graph Representations for Node Classification—0
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs—0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization—0
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering—0
Tackling Over-Smoothing for General Graph Convolutional Networks—0
Rethinking Graph Transformer Architecture Design for Node Classification—0
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering—0
Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis—0
Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective—0
Rethinking Tokenized Graph Transformers for Node Classification—0
Revisiting Graph Convolutional Network on Semi-Supervised Node Classification from an Optimization Perspective—0
Revisiting Graph Neural Networks for Link Prediction—0
Revisiting Heterophily in Graph Convolution Networks by Learning Representations Across Topological and Feature Spaces—0
Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing—0
Revisiting Over-smoothing in Deep GCNs—0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning—0
RLC-GNN: An Improved Deep Architecture for Spatial-Based Graph Neural Network with Application to Fraud Detection—0
NodeSig: Binary Node Embeddings via Random Walk Diffusion—0
Robustness Inspired Graph Backdoor Defense—0
Robust Subgraph Learning by Monitoring Early Training Representations—0
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling—0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—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
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