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 17011750 of 1860 papers

TitleStatusHype
FIT-GNN: Faster Inference Time for GNNs Using CoarseningCode0
Faster Graph Embeddings via CoarseningCode0
How Graph Structure and Label Dependencies Contribute to Node Classification in a Large Network of DocumentsCode0
Factorized Graph Representations for Semi-Supervised Learning from Sparse DataCode0
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph RepresentationsCode0
A robust feature reinforcement framework for heterogeneous graphs neural networksCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
Topology Attack and Defense for Graph Neural Networks: An Optimization PerspectiveCode0
Explicit Feature Interaction-aware Graph Neural NetworksCode0
Self-Enhanced GNN: Improving Graph Neural Networks Using Model OutputsCode0
Exact Certification of (Graph) Neural Networks Against Label PoisoningCode0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
CellTypeGraph: A New Geometric Computer Vision BenchmarkCode0
CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral FiltersCode0
Causality and Independence Enhancement for Biased Node ClassificationCode0
MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information NetworksCode0
Meta-GNN: On Few-shot Node Classification in Graph Meta-learningCode0
Meta-GPS++: Enhancing Graph Meta-Learning with Contrastive Learning and Self-TrainingCode0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
CatGCN: Graph Convolutional Networks with Categorical Node FeaturesCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
A Robust and Generalized Framework for Adversarial Graph EmbeddingCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised LearningCode0
Evaluating Deep Graph Neural NetworksCode0
Entropy Aware Message Passing in Graph Neural NetworksCode0
Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal TrainingCode0
MILE: A Multi-Level Framework for Scalable Graph EmbeddingCode0
A Robust Alternative for Graph Convolutional Neural Networks via Graph Neighborhood FiltersCode0
Torsion Graph Neural NetworksCode0
CAT: A Causally Graph Attention Network for Trimming Heterophilic GraphCode0
Enhancing the Expressivity of Temporal Graph Networks through Source-Target IdentificationCode0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
UniGAP: A Universal and Adaptive Graph Upsampling Approach to Mitigate Over-Smoothing in Node Classification TasksCode0
MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood MixingCode0
Towards a General Recipe for Combinatorial Optimization with Multi-Filter GNNsCode0
Towards a GML-Enabled Knowledge Graph PlatformCode0
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Enhanced Network Embedding with Text InformationCode0
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings AugmentationCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
End-to-End Learning on Multimodal Knowledge GraphsCode0
DeepGCNs: Can GCNs Go as Deep as CNNs?Code0
End-to-End Learning from Complex Multigraphs with Latent-Graph Convolutional NetworksCode0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
Modularity Optimization as a Training Criterion for Graph Neural NetworksCode0
Unsupervised Neighborhood Propagation Kernel Layers for Semi-supervised Node ClassificationCode0
Motif-based Convolutional Neural Network on GraphsCode0
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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
4Truncated KrylovAccuracy81.7Unverified
5SuperGAT MXAccuracy81.7Unverified
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