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 651–700 of 1860 papers

TitleStatusHype
Exploring the Potential of Large Language Models for Heterophilic Graphs—0
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification—0
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks—0
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts—0
Federated Graph Learning with Structure Proxy AlignmentCode0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
Graph Triple Attention Network: A Decoupled PerspectiveCode0
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling—0
DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Bootstrap Latents of Nodes and Neighbors for Graph Self-Supervised LearningCode0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Deep Generative Models for Subgraph PredictionCode0
Knowledge Probing for Graph Representation Learning—0
Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classification—0
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial IntelligenceCode0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification TasksCode0
Sharp Bounds for Poly-GNNs and the Effect of Graph Noise—0
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks—0
NC-NCD: Novel Class Discovery for Node ClassificationCode0
Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed Graphs—0
Revisiting Neighborhood Aggregation in Graph Neural Networks for Node Classification using Statistical Signal Processing—0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
GraphFM: A Scalable Framework for Multi-Graph Pretraining—0
Relaxing Graph Transformers for Adversarial Attacks—0
Graph Structure Prompt Learning: A Novel Methodology to Improve Performance of Graph Neural Networks—0
HyperAggregation: Aggregating over Graph Edges with HypernetworksCode0
Improving Hyperbolic Representations via Gromov-Wasserstein Regularization—0
GeoMix: Towards Geometry-Aware Data AugmentationCode0
Learning a Mini-batch Graph Transformer via Two-stage Interaction AugmentationCode0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
Conformal Inductive Graph Neural Networks—0
SlideGCD: Slide-based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image ClassificationCode0
STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMsCode0
SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network—0
Structure-Aware Consensus Network on Graphs with Few Labeled NodesCode0
A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs—0
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph ClassificationCode0
Leveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers—0
NTFormer: A Composite Node Tokenized Graph Transformer for Node Classification—0
Kolmogorov-Arnold Graph Neural Networks—0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Next Level Message-Passing with Hierarchical Support GraphsCode0
Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
A Pure Transformer Pretraining Framework on Text-attributed GraphsCode0
Multi-View Empowered Structural Graph Wordification for Language ModelsCode0
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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
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
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