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 351400 of 1860 papers

TitleStatusHype
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
CKGConv: General Graph Convolution with Continuous KernelsCode1
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data0
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching0
A Clean-graph Backdoor Attack against Graph Convolutional Networks with Poisoned Label Only0
You do not have to train Graph Neural Networks at all on text-attributed graphs0
Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis0
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer0
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices ApproachCode0
Hyperbolic Heterogeneous Graph Attention Networks0
VideoSAGE: Video Summarization with Graph Representation LearningCode2
Hierarchical Attention Models for Multi-Relational GraphsCode1
Fair Graph Neural Network with Supervised Contrastive Regularization0
Graph Neural Networks for Binary Programming0
Spectral Graph Pruning Against Over-Squashing and Over-SmoothingCode0
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural NetworksCode0
Generative-Contrastive Heterogeneous Graph Neural NetworkCode0
GLEMOS: Benchmark for Instantaneous Graph Learning Model SelectionCode0
HypeBoy: Generative Self-Supervised Representation Learning on HypergraphsCode1
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning0
Graph Neural Aggregation-diffusion with Metastability0
HealthGAT: Node Classifications in Electronic Health Records using Graph Attention NetworksCode0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification0
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural NetworkCode0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer0
Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks0
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures0
Exploring the Potential of Large Language Models in Graph Generation0
Open-World Semi-Supervised Learning for Node ClassificationCode1
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection0
Robust Subgraph Learning by Monitoring Early Training Representations0
Reduced Jeffries-Matusita distance: A Novel Loss Function to Improve Generalization Performance of Deep Classification Models0
Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning0
A Differential Geometric View and Explainability of GNN on Evolving Graphs0
L^2GC:Lorentzian Linear Graph Convolutional Networks for Node ClassificationCode0
Task-Oriented GNNs Training on Large Knowledge Graphs for Accurate and Efficient ModelingCode0
Spectral Invariant Learning for Dynamic Graphs under Distribution ShiftsCode1
Entropy Aware Message Passing in Graph Neural NetworksCode0
Better Schedules for Low Precision Training of Deep Neural Networks0
Polynormer: Polynomial-Expressive Graph Transformer in Linear TimeCode1
OpenGraph: Towards Open Graph Foundation ModelsCode3
Pairwise Alignment Improves Graph Domain AdaptationCode1
Decoupled Subgraph Federated LearningCode0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning0
GNNDLD: Graph Neural Network with Directional Label Distribution0
Hyperdimensional Representation Learning for Node Classification and Link Prediction0
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory0
Graph Parsing NetworksCode1
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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