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 351400 of 1860 papers

TitleStatusHype
On the Equivalence of Decoupled Graph Convolution Network and Label PropagationCode1
Should Graph Convolution Trust Neighbors? A Simple Causal Inference MethodCode1
Bayesian Attention ModulesCode1
Line Graph Neural Networks for Link PredictionCode1
Robust Optimization as Data Augmentation for Large-scale GraphsCode1
Factorizable Graph Convolutional NetworksCode1
Inductive Entity Representations from Text via Link PredictionCode1
Directional Graph NetworksCode1
Multi-hop Attention Graph Neural NetworkCode1
Learning Graph Normalization for Graph Neural NetworksCode1
Force2Vec: Parallel force-directed graph embeddingCode1
Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learningCode1
Understanding Coarsening for Embedding Large-Scale GraphsCode1
SNoRe: Scalable Unsupervised Learning of Symbolic Node RepresentationsCode1
Masked Label Prediction: Unified Message Passing Model for Semi-Supervised ClassificationCode1
Hierarchical Message-Passing Graph Neural NetworksCode1
Rethinking Graph Regularization for Graph Neural NetworksCode1
Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute InformationCode1
Mutual Teaching for Graph Convolutional NetworksCode1
Lifelong Graph LearningCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approachCode1
Optimization of Graph Neural Networks with Natural Gradient DescentCode1
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
Quaternion Graph Neural NetworksCode1
Second-Order Pooling for Graph Neural NetworksCode1
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphsCode1
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Towards Deeper Graph Neural NetworksCode1
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous GraphsCode1
Deep Learning for Abstract Argumentation SemanticsCode1
Graph Convolutional Networks for Graphs Containing Missing FeaturesCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural NetworksCode1
Simple and Deep Graph Convolutional NetworksCode1
Scaling Graph Neural Networks with Approximate PageRankCode1
Lifelong Learning of Graph Neural Networks for Open-World Node ClassificationCode1
Graph Prototypical Networks for Few-shot Learning on Attributed NetworksCode1
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural NetworksCode1
Self-supervised edge features for improved Graph Neural Network trainingCode1
A Self-Attention Network based Node Embedding ModelCode1
Graph BackdoorCode1
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective DesignsCode1
Backdoor Attacks to Graph Neural NetworksCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Adaptive Universal Generalized PageRank Graph Neural NetworkCode1
Data Augmentation for Graph Neural NetworksCode1
Locally Private Graph Neural NetworksCode1
Understanding Graph Neural Networks from Graph Signal Denoising PerspectivesCode1
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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