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 16011650 of 1860 papers

TitleStatusHype
Adversarial Attacks on Neural Networks for Graph DataCode0
Graph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block ModelsCode0
Large Language Model-driven Meta-structure Discovery in Heterogeneous Information NetworkCode0
Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node ClassificationCode0
Contrastive Meta-Learning for Few-shot Node ClassificationCode0
Contrastive Learning Meets Pseudo-label-assisted Mixup Augmentation: A Comprehensive Graph Representation Framework from Local to GlobalCode0
Large-Scale Learnable Graph Convolutional NetworksCode0
Geometric Scattering Attention NetworksCode0
RGL: A Simple yet Effective Relation Graph Augmented Prompt-based Tuning Approach for Few-Shot LearningCode0
LASE: Learned Adjacency Spectral EmbeddingsCode0
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural NetworksCode0
Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional NetworksCode0
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural NetworksCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Learnable Hypergraph Laplacian for Hypergraph LearningCode0
Learn from Heterophily: Heterophilous Information-enhanced Graph Neural NetworkCode0
Continuous Graph Neural NetworksCode0
Learning a Mini-batch Graph Transformer via Two-stage Interaction AugmentationCode0
Variational Embeddings for Community Detection and Node RepresentationCode0
Learning Based Proximity Matrix Factorization for Node EmbeddingCode0
Robust Graph Representation Learning via Neural SparsificationCode0
Connector 0.5: A unified framework for graph representation learningCode0
Learning Discrete Structures for Graph Neural NetworksCode0
A Simple and Scalable Graph Neural Network for Large Directed GraphsCode0
Learning from Heterogeneity: A Dynamic Learning Framework for HypergraphsCode0
Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label PropagationCode0
Geometric instability of graph neural networks on large graphsCode0
The Self-Loop Paradox: Investigating the Impact of Self-Loops on Graph Neural NetworksCode0
The Split Matters: Flat Minima Methods for Improving the Performance of GNNsCode0
Learning Label Initialization for Time-Dependent Harmonic ExtensionCode0
Geometric deep learning on graphs and manifolds using mixture model CNNsCode0
Generative-Contrastive Heterogeneous Graph Neural NetworkCode0
ROD: Reception-aware Online Distillation for Sparse GraphsCode0
A Simple and Yet Fairly Effective Defense for Graph Neural NetworksCode0
Learning on Large Graphs using Intersecting CommunitiesCode0
Role action embeddings: scalable representation of network positionsCode0
Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow VariantsCode0
Learning Representations using Spectral-Biased Random Walks on GraphsCode0
Learning Node Representations against PerturbationsCode0
ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative samplingCode0
Wide & Deep Learning for Node ClassificationCode0
Adversarial Attacks on Graph Neural Networks via Meta LearningCode0
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture SearchCode0
SA-MLP: Distilling Graph Knowledge from GNNs into Structure-Aware MLPCode0
Generalized Learning of Coefficients in Spectral Graph Convolutional NetworksCode0
Learning to Make Predictions on Graphs with AutoencodersCode0
Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary MetaheuristicsCode0
Generalized Laplacian Regularized Framelet Graph Neural NetworksCode0
TME-BNA: Temporal Motif-Preserving Network Embedding with Bicomponent Neighbor AggregationCode0
ABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation 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
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