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 251300 of 1860 papers

TitleStatusHype
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Mutual Teaching for Graph Convolutional NetworksCode1
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link PredictionCode1
Can GNN be Good Adapter for LLMs?Code1
CSGCL: Community-Strength-Enhanced Graph Contrastive LearningCode1
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype RepresentationCode1
GCNH: A Simple Method For Representation Learning On Heterophilous GraphsCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Characteristic Functions on Graphs: Birds of a Feather, from Statistical Descriptors to Parametric ModelsCode1
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective DesignsCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural NetworksCode1
Force2Vec: Parallel force-directed graph embeddingCode1
Robust Optimization as Data Augmentation for Large-scale GraphsCode1
From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning RevisitedCode1
GAP: Differentially Private Graph Neural Networks with Aggregation PerturbationCode1
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional NetworksCode1
Finding Global Homophily in Graph Neural Networks When Meeting HeterophilyCode1
From Hypergraph Energy Functions to Hypergraph Neural NetworksCode1
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Convolutional Neural Networks on Graphs with Chebyshev Approximation, RevisitedCode1
Confidence-Based Feature Imputation for Graphs with Partially Known FeaturesCode1
Correlation-Aware Graph Convolutional Networks for Multi-Label Node ClassificationCode1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-TrainingCode1
Adversarial Training Methods for Network EmbeddingCode1
CrossWalk: Fairness-enhanced Node Representation LearningCode1
CKGConv: General Graph Convolution with Continuous KernelsCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Data Augmentation for Graph Neural NetworksCode1
Feature Expansion for Graph Neural NetworksCode1
GIPA: General Information Propagation Algorithm for Graph LearningCode1
Class Label-aware Graph Anomaly DetectionCode1
GRAF: Graph Attention-aware Fusion NetworksCode1
Cleora: A Simple, Strong and Scalable Graph Embedding SchemeCode1
A Survey of Adversarial Learning on GraphsCode1
CLNode: Curriculum Learning for Node ClassificationCode1
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future DirectionsCode1
Fisher Information Embedding for Node and Graph LearningCode1
Deep Learning for Abstract Argumentation SemanticsCode1
Gated Graph Convolutional Recurrent Neural NetworksCode1
FastGCN: Fast Learning with Graph Convolutional Networks via Importance SamplingCode1
Graph-Bert: Only Attention is Needed for Learning Graph RepresentationsCode1
Graph Coloring with Physics-Inspired Graph Neural NetworksCode1
Graph Convolutional Networks for Graphs Containing Missing FeaturesCode1
A Self-Attention Network based Node Embedding ModelCode1
Combining Label Propagation and Simple Models Out-performs Graph Neural NetworksCode1
Deep Graph Contrastive Representation LearningCode1
A Survey on Role-Oriented Network EmbeddingCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
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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