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 301–350 of 1860 papers

TitleStatusHype
LinkGPT: Teaching Large Language Models To Predict Missing LinksCode1
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
NoisyGL: A Comprehensive Benchmark for Graph Neural Networks under Label NoiseCode1
Linear Opinion Pooling for Uncertainty Quantification on GraphsCode0
PANDA: Expanded Width-Aware Message Passing Beyond RewiringCode1
Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs—0
Learning Long Range Dependencies on Graphs via Random WalksCode1
Node-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach—0
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding—0
AGALE: A Graph-Aware Continual Learning Evaluation FrameworkCode0
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE—0
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary MetaheuristicsCode0
Sign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic GraphsCode0
Learning on Large Graphs using Intersecting CommunitiesCode0
Heterophilous Distribution Propagation for Graph Neural Networks—0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Fully-inductive Node Classification on Arbitrary GraphsCode2
Matrix Manifold Neural Networks++—0
Spatio-Spectral Graph Neural NetworksCode1
Unleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models—0
Graph Coarsening with Message-Passing Guarantees—0
Spectral Greedy Coresets for Graph Neural Networks—0
Node Identifiers: Compact, Discrete Representations for Efficient Graph LearningCode1
Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification—0
Encoder Embedding for General Graph and Node Classification—0
Sparse Spectral Training and Inference on Euclidean and Hyperbolic Neural Networks—0
AGS-GNN: Attribute-guided Sampling for Graph Neural NetworksCode0
Rethinking Independent Cross-Entropy Loss For Graph-Structured DataCode0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Automated Loss function Search for Class-imbalanced Node Classification—0
Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification—0
Similarity-Navigated Conformal Prediction for Graph Neural NetworksCode0
Node-Time Conditional Prompt Learning In Dynamic Graphs—0
Analysis of Corrected Graph Convolutions—0
LOGIN: A Large Language Model Consulted Graph Neural Network Training FrameworkCode0
Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural NetworksCode0
Conditional Shift-Robust Conformal Prediction for Graph Neural Network—0
Leveraging Discourse Structure for Extractive Meeting Summarization—0
Perception-Inspired Graph Convolution for Music Understanding TasksCode1
Certifying Robustness of Graph Convolutional Networks for Node Perturbation with Polyhedra Abstract Interpretation—0
A Survey of Large Language Models for GraphsCode3
Conditional Local Feature Encoding for Graph Neural Networks—0
Coefficient Decomposition for Spectral Graph ConvolutionCode0
SlotGAT: Slot-based Message Passing for Heterogeneous Graph Neural NetworkCode1
Lying Graph Convolution: Learning to Lie for Node Classification TasksCode0
IntraMix: Intra-Class Mixup Generation for Accurate Labels and NeighborsCode0
Uncertainty for Active Learning on Graphs—0
Generating Robust Counterfactual Witnesses for Graph Neural Networks—0
Training-free Graph Neural Networks and the Power of Labels as FeaturesCode2
Noisy Node Classification by Bi-level Optimization based Multi-teacher Distillation—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
5GEMAccuracy74.2—Unverified
6GGCMAccuracy74.2—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
4SuperGAT MXAccuracy81.7—Unverified
5Truncated KrylovAccuracy81.7—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