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
Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and MatchingCode1
SHGNN: Structure-Aware Heterogeneous Graph Neural NetworkCode1
SCR: Training Graph Neural Networks with Consistency RegularizationCode1
Augmentation-Free Self-Supervised Learning on GraphsCode1
Imbalanced Graph Classification via Graph-of-Graph Neural NetworksCode1
Understanding over-squashing and bottlenecks on graphs via curvatureCode1
HGATE: Heterogeneous Graph Attention Auto-EncodersCode1
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020Code1
On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node FeaturesCode1
p-Laplacian Based Graph Neural NetworksCode1
Simplifying approach to Node Classification in Graph Neural NetworksCode1
Cold Brew: Distilling Graph Node Representations with Incomplete or Missing NeighborhoodsCode1
Topological Relational Learning on GraphsCode1
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple MethodsCode1
VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector QuantizationCode1
Graph Posterior Network: Bayesian Predictive Uncertainty for Node ClassificationCode1
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
Boosting Graph Embedding on a Single GPUCode1
Graph-less Neural Networks: Teaching Old MLPs New Tricks via DistillationCode1
GRAPE for Fast and Scalable Graph Processing and random walk-based EmbeddingCode1
Topology-Imbalance Learning for Semi-Supervised Node ClassificationCode1
A Meta-Learning Approach for Training Explainable Graph Neural NetworksCode1
An Empirical Study of Graph Contrastive LearningCode1
Towards Self-Explainable Graph Neural NetworkCode1
Tree Decomposed Graph Neural NetworkCode1
Jointly Learnable Data Augmentations for Self-Supervised GNNsCode1
Global Self-Attention as a Replacement for Graph ConvolutionCode1
Relational Graph Convolutional Networks: A Closer LookCode1
A Survey on Role-Oriented Network EmbeddingCode1
DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network DataCode1
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed GraphsCode1
You are AllSet: A Multiset Function Framework for Hypergraph Neural NetworksCode1
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge GraphsCode1
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein ApproximationCode1
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learningCode1
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing MessagesCode1
Zero-shot Node Classification with Decomposed Graph Prototype NetworkCode1
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
Automated Self-Supervised Learning for GraphsCode1
XBNet : An Extremely Boosted Neural NetworkCode1
Graph-MLP: Node Classification without Message Passing in GraphCode1
NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled GraphsCode1
Pseudo-Riemannian Graph Convolutional NetworksCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Mixup for Node and Graph ClassificationCode1
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
Residual Network and Embedding Usage: New Tricks of Node Classification with Graph Convolutional NetworksCode1
Improving Graph Neural Networks with Simple Architecture DesignCode1
GIPA: General Information Propagation Algorithm for Graph LearningCode1
Accelerating Large Scale Real-Time GNN Inference using Channel PruningCode1
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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
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