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 13011350 of 1860 papers

TitleStatusHype
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification0
VECoDeR - Variational Embeddings for Community Detection and Node Representation0
VEM-GCN: Topology Optimization with Variational EM for Graph Convolutional Networks0
VersaGNN: a Versatile accelerator for Graph neural networks0
Vertex-Centric Visual Programming for Graph Neural Networks0
VGFL-SA: Vertical Graph Federated Learning Structure Attack Based on Contrastive Learning0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification0
Virtual Adversarial Training on Graph Convolutional Networks in Node Classification0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs0
Virtual Node Tuning for Few-shot Node Classification0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes0
Wasserstein Hypergraph Neural Network0
Watermarking Graph Neural Networks based on Backdoor Attacks0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification0
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer0
Weak Supervision for Real World Graphs0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks0
What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding0
When Does A Spectral Graph Neural Network Fail in Node Classification?0
When Do We Need Graph Neural Networks for Node Classification?0
YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training0
You do not have to train Graph Neural Networks at all on text-attributed graphs0
Zoo Guide to Network Embedding0
Zoom in to where it matters: a hierarchical graph based model for mammogram analysis0
Disambiguated Node Classification with Graph Neural NetworksCode0
Dimensional Reweighting Graph Convolutional NetworksCode0
MGC: A Complex-Valued Graph Convolutional Network for Directed GraphsCode0
SMGRL: Scalable Multi-resolution Graph Representation LearningCode0
Normalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node ClassificationCode0
Not All Neighbors are Friendly: Learning to Choose Hop Features to Improve Node ClassificationCode0
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNsCode0
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare AnalyticsCode0
Beyond Observed Connections : Link InjectionCode0
Diffusion-Jump GNNs: Homophiliation via Learnable Metric FiltersCode0
Graph Neural Networks Exponentially Lose Expressive Power for Node ClassificationCode0
On Calibration of Graph Neural Networks for Node ClassificationCode0
SoGCN: Second-Order Graph Convolutional NetworksCode0
Source Free Unsupervised Graph Domain AdaptationCode0
Training a Label-Noise-Resistant GNN with Reduced ComplexityCode0
On Generalized Degree Fairness in Graph Neural NetworksCode0
An Attention-based Graph Neural Network for Heterogeneous Structural LearningCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Sparse Graph Attention NetworksCode0
Diffusion-Convolutional Neural NetworksCode0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
On the choice of graph neural network architecturesCode0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
On the Design of Quantum Graph Convolutional Neural Network in the NISQ-Era and BeyondCode0
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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
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