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 12511300 of 1860 papers

TitleStatusHype
Topic-aware latent models for representation learning on networks0
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation0
Towards Federated Graph Learning in One-shot Communication0
Towards Label Position Bias in Graph Neural Networks0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification0
Towards Powerful Graph Neural Networks: Diversity Matters0
Towards Robust Graph Neural Networks against Label Noise0
Towards Unbiased Federated Graph Learning: Label and Topology Perspectives0
Towards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing0
Toward the Analysis of Graph Neural Networks0
TPGNN: Learning High-order Information in Dynamic Graphs via Temporal Propagation0
t-PINE: Tensor-based Predictable and Interpretable Node Embeddings0
Transfer Active Learning For Graph Neural Networks0
Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification0
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs0
Trivial bundle embeddings for learning graph representations0
Ultrahyperbolic Neural Networks0
Ultrahyperbolic Neural Networks0
Uncertainty-Aware Graph Self-Training with Expectation-Maximization Regularization0
Uncertainty-Aware Prediction for Graph Neural Networks0
Uncertainty-Aware Robust Learning on Noisy Graphs0
Uncertainty for Active Learning on Graphs0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks0
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks0
Uncertainty Propagation in Node Classification0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective0
Understanding graph embedding methods and their applications0
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory0
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems0
Unified Robust Training for Graph NeuralNetworks against Label Noise0
Unifying Graph Convolutional Networks as Matrix Factorization0
Unifying Homophily and Heterophily Network Transformation via Motifs0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding0
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models0
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing0
Universal Graph Continual Learning0
Universal Network Representation for Heterogeneous Information Networks0
Unlearning Algorithmic Biases over Graphs0
Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models0
Unsupervised Adversarially-Robust Representation Learning on Graphs0
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees0
Unsupervised Domain-adaptive Hash for Networks0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models0
Unsupervised Learning of Node Embeddings by Detecting Communities0
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information0
Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs0
Using Graph Algorithms to Pretrain Graph Completion Transformers0
Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach0
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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
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
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