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 851900 of 1860 papers

TitleStatusHype
Explainability-Based Adversarial Attack on Graphs Through Edge Perturbation0
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
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
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity0
Dynamic Spiking Framework for Graph Neural Networks0
GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy0
CAT: A Causally Graph Attention Network for Trimming Heterophilic GraphCode0
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
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification0
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
Node Classification in Random TreesCode0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs0
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation0
Predicting Properties of Nodes via Community-Aware FeaturesCode0
Edge2Node: Reducing Edge Prediction to Node Classification0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach0
BLIS-Net: Classifying and Analyzing Signals on Graphs0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Graph Neural Networks with a Distribution of Parametrized Graphs0
Graph Agent: Explicit Reasoning Agent for Graphs0
Hierarchical Randomized Smoothing0
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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