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 451–500 of 1860 papers

TitleStatusHype
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information AggregationCode0
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering—0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Population Graph Cross-Network Node Classification for Autism Detection Across Sample GroupsCode0
Predicting the structure of dynamic graphs—0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation—0
Multimodal weighted graph representation for information extraction from visually rich documents.Code0
Effective backdoor attack on graph neural networks in link prediction tasks—0
Strong Transitivity Relations and Graph Neural NetworksCode0
A clean-label graph backdoor attack method in node classification task—0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation—0
Mitigating Degree Biases in Message Passing Mechanism by Utilizing Community StructuresCode1
Hierarchical Aggregations for High-Dimensional Multiplex Graph EmbeddingCode0
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks—0
RDF-star2Vec: RDF-star Graph Embeddings for Data MiningCode0
Graph Coarsening via Convolution Matching for Scalable Graph Neural Network TrainingCode0
Towards Fine-Grained Explainability for Heterogeneous Graph Neural NetworkCode0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)Code0
NodeMixup: Tackling Under-Reaching for Graph Neural NetworksCode0
Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding—0
Chasing Fairness in Graphs: A GNN Architecture PerspectiveCode0
Graph Transformers for Large GraphsCode1
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
Hypergraph Transformer for Semi-Supervised ClassificationCode1
Dynamic Spiking Framework for Graph Neural Networks—0
Hypergraph-MLP: Learning on Hypergraphs without Message PassingCode1
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy—0
Graph Neural Networks with Diverse Spectral FilteringCode1
CAT: A Causally Graph Attention Network for Trimming Heterophilic GraphCode0
ERASE: Error-Resilient Representation Learning on Graphs for Label Noise ToleranceCode1
Curriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNsCode0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks—0
On the Initialization of Graph Neural NetworksCode0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural NetworksCode0
Tracing Hyperparameter Dependencies for Model Parsing via Learnable Graph Pooling NetworkCode0
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks—0
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs—0
On the Adversarial Robustness of Graph Contrastive Learning Methods—0
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training—0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs—0
Hard Label Black Box Node Injection Attack on Graph Neural NetworksCode0
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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