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 9511000 of 1860 papers

TitleStatusHype
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active LearningCode0
Independent Distribution Regularization for Private Graph EmbeddingCode0
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification0
Node Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights0
G^2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns0
Local Structure-aware Graph Contrastive Representation Learning0
Communication-Free Distributed GNN Training with Vertex Cut0
Label Inference Attacks against Node-level Vertical Federated GNNs0
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
Feature Transportation Improves Graph Neural Networks0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
Learning Adaptive Neighborhoods for Graph Neural Networks0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs0
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach0
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines0
Supervised Attention Using Homophily in Graph Neural Networks0
Learning from Heterogeneity: A Dynamic Learning Framework for HypergraphsCode0
TransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformersCode0
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks0
A Survey on Graph Classification and Link Prediction based on GNN0
Re-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion FunctionalsCode0
Diffusion-Jump GNNs: Homophiliation via Learnable Metric FiltersCode0
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks0
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily0
Torsion Graph Neural NetworksCode0
Geometric Pooling: maintaining more useful information0
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks0
Contrastive Disentangled Learning on Graph for Node Classification0
Mixed-Curvature Transformers for Graph Representation Learning papersreview0
Dual Node and Edge Fairness-Aware Graph Partition0
The Split Matters: Flat Minima Methods for Improving the Performance of GNNsCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Learning on Graphs under Label Noise0
A Simple and Scalable Graph Neural Network for Large Directed GraphsCode0
Inductive Linear Probing for Few-shot Node Classification0
Uncertainty-Aware Robust Learning on Noisy Graphs0
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy0
CARL-G: Clustering-Accelerated Representation Learning on Graphs0
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience0
Virtual Node Tuning for Few-shot Node Classification0
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks0
BeMap: Balanced Message Passing for Fair Graph Neural NetworkCode0
Fast and Effective GNN Training with Linearized Random Spanning Trees0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness0
Randomized Schur Complement Views for Graph Contrastive LearningCode0
Optimal Inference in Contextual Stochastic Block Models0
Explaining and Adapting Graph Conditional Shift0
Clarify Confused Nodes via Separated LearningCode0
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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