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 10511100 of 1860 papers

TitleStatusHype
Framelet Message Passing0
Scalable Neural Network Training over Distributed GraphsCode0
GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification0
Random Projection Forest Initialization for Graph Convolutional NetworksCode0
Graph Construction using Principal Axis Trees for Simple Graph ConvolutionCode0
Diffusion Probabilistic Models for Structured Node Classification0
Finding Heterophilic Neighbors via Confidence-based Subgraph Matching for Semi-supervised Node Classification0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks0
On Generalized Degree Fairness in Graph Neural NetworksCode0
Heterophily-Aware Graph Attention Network0
Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search0
Simple yet Effective Gradient-Free Graph Convolutional Networks0
OrthoReg: Improving Graph-regularized MLPs via Orthogonality Regularization0
KG-Hub -- Building and Exchanging Biological Knowledge Graphs0
Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive LearningCode0
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node ClassificationCode0
SplitGNN: Splitting GNN for Node Classification with Heterogeneous Attention0
Federated Learning over Coupled Graphs0
Graph Neural Tangent Kernel: Convergence on Large Graphs0
Feature Selection: Key to Enhance Node Classification with Graph Neural NetworksCode0
Neighborhood Homophily-based Graph Convolutional NetworkCode0
Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks0
Weakly Supervised Joint Whole-Slide Segmentation and Classification in Prostate Cancer0
Few-shot Node Classification with Extremely Weak SupervisionCode0
GUAP: Graph Universal Attack Through Adversarial Patching0
Domain Adaptive Graph Infomax via Conditional Adversarial NetworksCode0
NetEffect: Discovery and Exploitation of Generalized Network EffectsCode0
GANExplainer: GAN-based Graph Neural Networks Explainer0
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings0
PersonaSAGE: A Multi-Persona Graph Neural Network0
2-hop Neighbor Class Similarity (2NCS): A graph structural metric indicative of graph neural network performance0
Refined Edge Usage of Graph Neural Networks for Edge Prediction0
Multi-duplicated Characterization of Graph Structures using Information Gain Ratio for Graph Neural Networks0
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Neural Networks0
On the Design of Quantum Graph Convolutional Neural Network in the NISQ-Era and BeyondCode0
Leave Graphs Alone: Addressing Over-Squashing without Rewiring0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges0
Adversarial Weight Perturbation Improves Generalization in Graph Neural NetworksCode0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples0
Dynamic Graph Node Classification via Time Augmentation0
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids0
Dissimilar Nodes Improve Graph Active LearningCode0
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point ProcessesCode0
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature LearningCode0
Semantic Graph Neural Network with Multi-measure Learning for Semi-supervised Classification0
Hierarchical Model Selection for Graph Neural Netoworks0
Every Node Counts: Improving the Training of Graph Neural Networks on Node Classification0
Text Representation Enrichment Utilizing Graph based Approaches: Stock Market Technical Analysis Case Study0
Mitigating Overfitting in Graph Neural Networks via Feature and Hyperplane Perturbation0
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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