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 451500 of 1860 papers

TitleStatusHype
Infinite-Horizon Graph Filters: Leveraging Power Series to Enhance Sparse Information AggregationCode0
Rethinking Spectral Graph Neural Networks with Spatially Adaptive Filtering0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation0
Population Graph Cross-Network Node Classification for Autism Detection Across Sample GroupsCode0
Predicting the structure of dynamic graphs0
SynHING: Synthetic Heterogeneous Information Network Generation for Graph Learning and Explanation0
Multimodal weighted graph representation for information extraction from visually rich documents.Code0
Effective backdoor attack on graph neural networks in link prediction tasks0
Strong Transitivity Relations and Graph Neural NetworksCode0
A clean-label graph backdoor attack method in node classification task0
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation0
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 Networks0
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 Embedding0
Chasing Fairness in Graphs: A GNN Architecture PerspectiveCode0
Graph Transformers for Large GraphsCode1
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity0
Hypergraph Transformer for Semi-Supervised ClassificationCode1
Dynamic Spiking Framework for Graph Neural Networks0
Hypergraph-MLP: Learning on Hypergraphs without Message PassingCode1
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy0
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 Learning0
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning0
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks0
On the Initialization of Graph Neural NetworksCode0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More0
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 Networks0
Propagate & Distill: Towards Effective Graph Learners Using Propagation-Embracing MLPs0
On the Adversarial Robustness of Graph Contrastive Learning Methods0
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Large Language Models as Topological Structure Enhancers for Text-Attributed Graphs0
Hard Label Black Box Node Injection Attack on Graph Neural NetworksCode0
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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
6NodeNetAccuracy86.8Unverified
7CleoraAccuracy86.8Unverified
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