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 10511100 of 1860 papers

TitleStatusHype
Network Vector: Distributed Representations of Networks with Global Context0
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network0
Neurally boosted supervised spectral clustering0
Neural Models for Output-Space Invariance in Combinatorial Problems0
Neural Networks in a Product of Hyperbolic Spaces0
Neural Trees for Learning on Graphs0
Next Waves in Veridical Network Embedding0
node2coords: Graph Representation Learning with Wasserstein Barycenters0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks0
Node Classification and Search on the Rubik's Cube Graph with GNNs0
Node Classification in Uncertain Graphs0
Node Classification Meets Link Prediction on Knowledge Graphs0
Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks0
Node Classification With Integrated Reject Option0
Node Copying: A Random Graph Model for Effective Graph Sampling0
Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights0
Node Injection Attacks on Graphs via Reinforcement Learning0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning0
Node Masking: Making Graph Neural Networks Generalize and Scale Better0
NodeNet: A Graph Regularised Neural Network for Node Classification0
NodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistency0
Node Representation Learning for Directed Graphs0
NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME0
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks0
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach0
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation0
Non-Parametric Graph Learning for Bayesian Graph Neural Networks0
Non-Recursive Graph Convolutional Networks0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks0
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification0
Octave Graph Convolutional Network0
On Classification Thresholds for Graph Attention with Edge Features0
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs0
Hyperdimensional Representation Learning for Node Classification and Link Prediction0
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods0
On Local Aggregation in Heterophilic Graphs0
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks0
On the Adversarial Robustness of Graph Contrastive Learning Methods0
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations0
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks0
Optimal Inference in Contextual Stochastic Block Models0
Optimality of Message-Passing Architectures for Sparse Graphs0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks0
Ordinary differential equations on graph networks0
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization0
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation0
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations0
Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification0
Over-Squashing in Graph Neural Networks: A Comprehensive survey0
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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
10DifNetAccuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GGCMAccuracy74.2Unverified
6GEMAccuracy74.2Unverified
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