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 601650 of 1860 papers

TitleStatusHype
SimTeG: A Frustratingly Simple Approach Improves Textual Graph LearningCode1
UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node ClassificationCode1
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node ClassificationCode0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs0
Feature Transportation Improves Graph Neural Networks0
Addressing the Impact of Localized Training Data in Graph Neural NetworksCode0
OpenGDA: Graph Domain Adaptation Benchmark for Cross-network LearningCode1
Learning Adaptive Neighborhoods for Graph Neural Networks0
Examining the Effects of Degree Distribution and Homophily in Graph Learning ModelsCode1
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs0
PolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point CloudsCode1
Long-range Meta-path Search on Large-scale Heterogeneous GraphsCode1
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines0
Supervised Attention Using Homophily in Graph Neural Networks0
Towards Fair Graph Neural Networks via Graph CounterfactualCode1
Learning from Heterogeneity: A Dynamic Learning Framework for HypergraphsCode0
Exploring the Potential of Large Language Models (LLMs) in Learning on GraphsCode2
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
Unsupervised Episode Generation for Graph Meta-learningCode1
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks0
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily0
Boosting Multitask Learning on Graphs through Higher-Order Task AffinitiesCode1
Torsion Graph Neural NetworksCode0
Structure-Aware DropEdge Towards Deep Graph Convolutional Networks0
Geometric Pooling: maintaining more useful information0
Contrastive Disentangled Learning on Graph for Node Classification0
CAT-Walk: Inductive Hypergraph Learning via Set WalksCode1
Mixed-Curvature Transformers for Graph Representation Learning papersreview0
Dual Node and Edge Fairness-Aware Graph Partition0
From Hypergraph Energy Functions to Hypergraph Neural NetworksCode1
GraphSHA: Synthesizing Harder Samples for Class-Imbalanced Node ClassificationCode1
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
The Split Matters: Flat Minima Methods for Improving the Performance of GNNsCode0
Uncertainty-Aware Robust Learning on Noisy Graphs0
Inductive Linear Probing for Few-shot Node Classification0
A Simple and Scalable Graph Neural Network for Large Directed GraphsCode0
Learning on Graphs under Label Noise0
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy0
NodeFormer: A Scalable Graph Structure Learning Transformer for Node ClassificationCode2
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience0
CARL-G: Clustering-Accelerated Representation Learning on Graphs0
Virtual Node Tuning for Few-shot Node Classification0
On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks0
Fast and Effective GNN Training with Linearized Random Spanning Trees0
Show:102550
← PrevPage 13 of 38Next →

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
10TransGNN1:1 Accuracy85.1Unverified
#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