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 801850 of 1860 papers

TitleStatusHype
Distribution Free Prediction Sets for Node ClassificationCode0
GREAD: Graph Neural Reaction-Diffusion NetworksCode1
Multi-Mask Aggregators for Graph Neural NetworksCode1
MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural NetworksCode1
Enhancing Intra-class Information Extraction for Heterophilous Graphs: One Neural Architecture Search Approach0
Unifying Label-inputted Graph Neural Networks with Deep Equilibrium ModelsCode0
A robust feature reinforcement framework for heterogeneous graphs neural networksCode0
Prototype-based Interpretable Graph Neural NetworksCode0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification0
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
Graph Contrastive Learning with Implicit AugmentationsCode0
A Spectral Analysis of Graph Neural Networks on Dense and Sparse GraphsCode0
Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node ClassifiersCode0
Revisiting Heterophily in Graph Convolution Networks by Learning Representations Across Topological and Feature Spaces0
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning NetworksCode2
Learning Heuristics for the Maximum Clique Enumeration Problem Using Low Dimensional Representations0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process0
When Do We Need Graph Neural Networks for Node Classification?0
Localized Randomized Smoothing for Collective Robustness Certification0
Beyond Homophily with Graph Echo State Networks0
A Graph Is More Than Its Nodes: Towards Structured Uncertainty-Aware Learning on GraphsCode0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
Explaining the Explainers in Graph Neural Networks: a Comparative StudyCode1
Line Graph Contrastive Learning for Link PredictionCode0
FocusedCleaner: Sanitizing Poisoned Graphs for Robust GNN-based Node Classification0
Binary Graph Convolutional Network with Capacity ExplorationCode1
HCL: Improving Graph Representation with Hierarchical Contrastive Learning0
Graph Few-shot Learning with Task-specific StructuresCode0
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs0
Causally-guided Regularization of Graph Attention Improves Generalizability0
Analysis of Convolutions, Non-linearity and Depth in Graph Neural Networks using Neural Tangent KernelCode0
On Classification Thresholds for Graph Attention with Edge Features0
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLPCode0
AdaGCL: Adaptive Subgraph Contrastive Learning to Generalize Large-scale Graph TrainingCode0
Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node ClassificationCode0
Improving Your Graph Neural Networks: A High-Frequency BoosterCode0
MGNNI: Multiscale Graph Neural Networks with Implicit LayersCode1
Augmentation-Free Graph Contrastive Learning of Invariant-Discriminative RepresentationsCode3
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and RethinkingCode1
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
Revisiting Heterophily For Graph Neural NetworksCode1
Using Graph Algorithms to Pretrain Graph Completion Transformers0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation0
JuryGCN: Quantifying Jackknife Uncertainty on Graph Convolutional Networks0
Linkless Link Prediction via Relational Distillation0
Uplifting Message Passing Neural Network with Graph Original Information0
Towards Real-Time Temporal Graph LearningCode0
Less is More: SlimG for Accurate, Robust, and Interpretable Graph MiningCode0
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information0
Teaching Yourself: Graph Self-Distillation on Neighborhood for Node Classification0
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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
6CleoraAccuracy86.8Unverified
7NodeNetAccuracy86.8Unverified
8MMAAccuracy85.8Unverified
9GResNet(GAT)Accuracy85.5Unverified
10TransGNN1:1 Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GEMAccuracy74.2Unverified
6GGCMAccuracy74.2Unverified
7Truncated KrylovAccuracy73.86Unverified
8SSGCAccuracy73.6Unverified
9OKDEEMAccuracy73.53Unverified
10GCNIIAccuracy73.4Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4Unverified
2CPF-tra-GCNIIAccuracy83.2Unverified
3DSGCNAccuracy81.9Unverified
4Truncated KrylovAccuracy81.7Unverified
5SuperGAT MXAccuracy81.7Unverified
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