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
Recipe for a General, Powerful, Scalable Graph TransformerCode2
Recipe2Vec: Multi-modal Recipe Representation Learning with Graph Neural NetworksCode1
High-Order Pooling for Graph Neural Networks with Tensor Decomposition0
Compressing Deep Graph Neural Networks via Adversarial Knowledge DistillationCode1
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification0
GraphMAE: Self-Supervised Masked Graph AutoencodersCode2
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich NetworksCode1
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs0
On the Prediction Instability of Graph Neural NetworksCode0
FairNorm: Fair and Fast Graph Neural Network Training0
Towards Explanation for Unsupervised Graph-Level Representation LearningCode2
What's Behind the Mask: Understanding Masked Graph Modeling for Graph AutoencodersCode6
Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis0
A Simple Yet Effective SVD-GCN for Directed GraphsCode0
Simplifying Node Classification on Heterophilous Graphs with Compatible Label PropagationCode0
CellTypeGraph: A New Geometric Computer Vision BenchmarkCode0
Finding Global Homophily in Graph Neural Networks When Meeting HeterophilyCode1
NDGGNET-A Node Independent Gate based Graph Neural Networks0
A Survey on Fairness for Machine Learning on GraphsCode0
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks0
Bandits for Structure Perturbation-based Black-box Attacks to Graph Neural Networks with Theoretical GuaranteesCode0
Optimal Propagation for Graph Neural Networks0
LPGNet: Link Private Graph Networks for Node ClassificationCode0
Learning Label Initialization for Time-Dependent Harmonic ExtensionCode0
FastGCL: Fast Self-Supervised Learning on Graphs via Contrastive Neighborhood Aggregation0
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive LearningCode1
Detecting Topology Attacks against Graph Neural Networks0
Effects of Graph Convolutions in Multi-layer Networks0
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network0
Label Efficient Regularization and Propagation for Graph Node Classification0
BSAL: A Framework of Bi-component Structure and Attribute Learning for Link PredictionCode0
Neural Structured Prediction for Inductive Node ClassificationCode1
ERGO: Event Relational Graph Transformer for Document-level Event Causality Identification0
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
Inferring from References with Differences for Semi-Supervised Node Classification on GraphsCode0
Multi-view graph structure learning using subspace merging on Grassmann manifold0
Graph Ordering Attention NetworksCode1
Explicit Feature Interaction-aware Graph Neural NetworksCode0
Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020Code0
A Survey on Graph Representation Learning Methods0
GraFN: Semi-Supervised Node Classification on Graph with Few Labels via Non-Parametric Distribution AssignmentCode1
Synthetic Graph Generation to Benchmark Graph Learning0
Message Passing Neural Networks for Hypergraphs0
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation0
Supervised Graph Contrastive Learning for Few-shot Node Classification0
TGL: A General Framework for Temporal GNN Training on Billion-Scale GraphsCode2
Node Representation Learning in Graph via Node-to-Neighbourhood Mutual Information MaximizationCode1
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks0
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node SamplingCode1
Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with HeterophilyCode1
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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