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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1801–1850 of 1860 papers

TitleStatusHype
Triple2Vec: Learning Triple Embeddings from Knowledge Graphs—0
Trivial bundle embeddings for learning graph representations—0
Ultrahyperbolic Neural Networks—0
Ultrahyperbolic Neural Networks—0
Uncertainty-Aware Graph Self-Training with Expectation-Maximization Regularization—0
Uncertainty-Aware Prediction for Graph Neural Networks—0
Uncertainty-Aware Robust Learning on Noisy Graphs—0
Uncertainty for Active Learning on Graphs—0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks—0
Uncertainty-Matching Graph Neural Networks to Defend Against Poisoning Attacks—0
Uncertainty Propagation in Node Classification—0
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective—0
Understanding graph embedding methods and their applications—0
Understanding Oversmoothing in Diffusion-Based GNNs From the Perspective of Operator Semigroup Theory—0
Unified Graph Networks (UGN): A Deep Neural Framework for Solving Graph Problems—0
Unified Robust Training for Graph NeuralNetworks against Label Noise—0
Unifying Graph Convolutional Networks as Matrix Factorization—0
Unifying Homophily and Heterophily Network Transformation via Motifs—0
Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding—0
Unifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models—0
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing—0
Universal Graph Continual Learning—0
Universal Network Representation for Heterogeneous Information Networks—0
Unlearning Algorithmic Biases over Graphs—0
Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Unsupervised Attributed Dynamic Network Embedding with Stability Guarantees—0
Unsupervised Domain-adaptive Hash for Networks—0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models—0
Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models—0
Unsupervised Learning of Node Embeddings by Detecting Communities—0
Unsupervised Semantic Representation Learning of Scientific Literature Based on Graph Attention Mechanism and Maximum Mutual Information—0
Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs—0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Using Graph Algorithms to Pretrain Graph Completion Transformers—0
Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach—0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
VECoDeR - Variational Embeddings for Community Detection and Node Representation—0
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks—0
VersaGNN: a Versatile accelerator for Graph neural networks—0
Vertex-Centric Visual Programming for Graph Neural Networks—0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning—0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification—0
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification—0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Virtual Node Tuning for Few-shot Node Classification—0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Wasserstein Hypergraph Neural Network—0
Watermarking Graph Neural Networks based on Backdoor Attacks—0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Show:102550
← PrevPage 37 of 38Next →
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