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 201250 of 1860 papers

TitleStatusHype
Compressing Deep Graph Neural Networks via Adversarial Knowledge DistillationCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein ApproximationCode1
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and RethinkingCode1
Beyond Homophily: Structure-aware Path Aggregation Graph Neural NetworkCode1
EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph LearningCode1
Beyond Low-frequency Information in Graph Convolutional NetworksCode1
An Empirical Study of Graph Contrastive LearningCode1
Graph Random Neural Network for Semi-Supervised Learning on GraphsCode1
Efficient Graph Deep Learning in TensorFlow with tf_geometricCode1
GraphSAINT: Graph Sampling Based Inductive Learning MethodCode1
CAT-Walk: Inductive Hypergraph Learning via Set WalksCode1
Graph Stochastic Neural Networks for Semi-supervised LearningCode1
Adversarial Training Methods for Network EmbeddingCode1
Graph Transformers for Large GraphsCode1
TREE-G: Decision Trees Contesting Graph Neural NetworksCode1
GREAD: Graph Neural Reaction-Diffusion NetworksCode1
Half-Hop: A graph upsampling approach for slowing down message passingCode1
Bilinear Graph Neural Network with Neighbor InteractionsCode1
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich NetworksCode1
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
Binary Graph Convolutional Network with Capacity ExplorationCode1
Accelerating Large Scale Real-Time GNN Inference using Channel PruningCode1
CrossWalk: Fairness-enhanced Node Representation LearningCode1
Deep Graph Contrastive Representation LearningCode1
BNS-GCN: Efficient Full-Graph Training of Graph Convolutional Networks with Partition-Parallelism and Random Boundary Node SamplingCode1
Higher-order Graph Convolutional Network with Flower-Petals Laplacians on Simplicial ComplexesCode1
Random Laplacian Features for Learning with Hyperbolic SpaceCode1
Boosting Graph Embedding on a Single GPUCode1
A pipeline for fair comparison of graph neural networks in node classification tasksCode1
Boosting Multitask Learning on Graphs through Higher-Order Task AffinitiesCode1
Hypergraph-Induced Semantic Tuplet Loss for Deep Metric LearningCode1
Semi-supervised Hypergraph Node Classification on Hypergraph Line ExpansionCode1
Breaking the Limit of Graph Neural Networks by Improving the Assortativity of Graphs with Local Mixing PatternsCode1
IGB: Addressing The Gaps In Labeling, Features, Heterogeneity, and Size of Public Graph Datasets for Deep Learning ResearchCode1
A Proposal of Multi-Layer Perceptron with Graph Gating Unit for Graph Representation Learning and its Application to Surrogate Model for FEMCode1
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph NetworksCode1
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing MessagesCode1
SCR: Training Graph Neural Networks with Consistency RegularizationCode1
Inductive Entity Representations from Text via Link PredictionCode1
Inductive Representation Learning on Large GraphsCode1
Disentangled Condensation for Large-scale GraphsCode1
Adversarial Deep Network Embedding for Cross-network Node ClassificationCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Jointly Learnable Data Augmentations for Self-Supervised GNNsCode1
A Representation Learning Framework for Property GraphsCode1
Label-free Node Classification on Graphs with Large Language Models (LLMS)Code1
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple MethodsCode1
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
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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
10DifNetAccuracy85.1Unverified
#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