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 901950 of 1860 papers

TitleStatusHype
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos0
The DLCC Node Classification Benchmark for Analyzing Knowledge Graph EmbeddingsCode1
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks0
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
Graph Generative Model for Benchmarking Graph Neural NetworksCode1
On Graph Neural Network Fairness in the Presence of Heterophilous Neighborhoods0
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling0
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
What Do Graph Convolutional Neural Networks Learn?Code0
Neural Networks in a Product of Hyperbolic Spaces0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot Learning0
Assessing the Effects of Hyperparameters on Knowledge Graph Embedding QualityCode0
Modularity Optimization as a Training Criterion for Graph Neural NetworksCode0
A Representation Learning Framework for Property GraphsCode1
Structural Entropy Guided Graph Hierarchical PoolingCode1
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node ClassificationCode1
Geometry Contrastive Learning on Heterogeneous GraphsCode0
Task-Adaptive Few-shot Node ClassificationCode1
Similarity-aware Positive Instance Sampling for Graph Contrastive Pre-training0
Understanding convolution on graphs via energiesCode1
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural NetworksCode0
Propagation with Adaptive Mask then Training for Node Classification on Attributed Networks0
GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural NetworksCode1
Beyond Real-world Benchmark Datasets: An Empirical Study of Node Classification with GNNsCode0
Sheaf Neural Networks with Connection LaplaciansCode1
DFG-NAS: Deep and Flexible Graph Neural Architecture SearchCode0
Long Range Graph BenchmarkCode1
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization0
CLNode: Curriculum Learning for Node ClassificationCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
GraphFM: Improving Large-Scale GNN Training via Feature Momentum0
Superiority of GNN over NN in generalizing bandlimited functions0
ACMP: Allen-Cahn Message Passing for Graph Neural Networks with Particle Phase TransitionCode1
Semi-Supervised Hierarchical Graph Classification0
Synthetic Over-sampling for Imbalanced Node Classification with Graph Neural Networks0
Fundamental Limits in Formal Verification of Message-Passing Neural Networks0
NAGphormer: A Tokenized Graph Transformer for Node Classification in Large GraphsCode1
Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link PredictionCode1
I'm Me, We're Us, and I'm Us: Tri-directional Contrastive Learning on HypergraphsCode1
A Unification Framework for Euclidean and Hyperbolic Graph Neural NetworksCode0
Alternately Optimized Graph Neural Networks0
Mixed Graph Contrastive Network for Semi-Supervised Node Classification0
Restructuring Graph for Higher Homophily via Adaptive Spectral Clustering0
On Calibration of Graph Neural Networks for Node ClassificationCode0
Automatic Relation-aware Graph Network ProliferationCode1
Label-Enhanced Graph Neural Network for Semi-supervised Node ClassificationCode1
Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing the Over-smoothing0
Bayesian Robust Graph Contrastive LearningCode0
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype RepresentationCode1
EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural NetworksCode1
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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
10TransGNN1:1 Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GEMAccuracy74.2Unverified
6GGCMAccuracy74.2Unverified
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