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 1601–1650 of 1860 papers

TitleStatusHype
Node Copying: A Random Graph Model for Effective Graph Sampling—0
Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights—0
Node Injection Attacks on Graphs via Reinforcement Learning—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Node Masking: Making Graph Neural Networks Generalize and Scale Better—0
NodeNet: A Graph Regularised Neural Network for Node Classification—0
NodeReg: Mitigating the Imbalance and Distribution Shift Effects in Semi-Supervised Node Classification via Norm Consistency—0
Node Representation Learning for Directed Graphs—0
NODE-SELECT: A FLEXIBLE GRAPH NEURAL NETWORK BASED ON REALISTIC PROPAGATION SCHEME—0
Node-Specific Space Selection via Localized Geometric Hyperbolicity in Graph Neural Networks—0
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach—0
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation—0
Non-Parametric Graph Learning for Bayesian Graph Neural Networks—0
Non-Recursive Graph Convolutional Networks—0
NP^2L: Negative Pseudo Partial Labels Extraction for Graph Neural Networks—0
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification—0
Octave Graph Convolutional Network—0
On Classification Thresholds for Graph Attention with Edge Features—0
One Model for One Graph: A New Perspective for Pretraining with Cross-domain Graphs—0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods—0
On Local Aggregation in Heterophilic Graphs—0
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
On the Impact of Feature Heterophily on Link Prediction with Graph Neural Networks—0
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations—0
Optimal Exact Recovery in Semi-Supervised Learning: A Study of Spectral Methods and Graph Convolutional Networks—0
Optimal Inference in Contextual Stochastic Block Models—0
Optimality of Message-Passing Architectures for Sparse Graphs—0
Optimizing Blockchain Analysis: Tackling Temporality and Scalability with an Incremental Approach with Metropolis-Hastings Random Walks—0
Ordinary differential equations on graph networks—0
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization—0
Out-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation—0
Overcoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations—0
Overlap-aware meta-learning attention to enhance hypergraph neural networks for node classification—0
Over-Squashing in Graph Neural Networks: A Comprehensive survey—0
Pair-view Unsupervised Graph Representation Learning—0
PanRep: Universal node embeddings for heterogeneous graphs—0
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Pave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics—0
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks—0
Permutohedral-GCN: Graph Convolutional Networks with Global Attention—0
Personalized Layer Selection for Graph Neural Networks—0
PersonaSAGE: A Multi-Persona Graph Neural Network—0
Perturbation Ontology based Graph Attention Networks—0
PI-GNN: Towards Robust Semi-Supervised Node Classification against Noisy Labels—0
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions—0
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding—0
PolicyClusterGCN: Identifying Efficient Clusters for Training 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
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
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