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 401450 of 1860 papers

TitleStatusHype
Geometer: Graph Few-Shot Class-Incremental Learning via Prototype RepresentationCode1
Replay-and-Forget-Free Graph Class-Incremental Learning: A Task Profiling and Prompting ApproachCode1
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance DecompositionCode1
Revisiting Heterophily For Graph Neural NetworksCode1
DRew: Dynamically Rewired Message Passing with DelayCode1
FastGCN: Fast Learning with Graph Convolutional Networks via Importance SamplingCode1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Can LLMs Effectively Leverage Graph Structural Information through Prompts, and Why?Code1
Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation FrameworkCode1
Diffusion Mechanism in Residual Neural Network: Theory and ApplicationsCode1
A Comprehensive Study on Large-Scale Graph Training: Benchmarking and RethinkingCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
Beyond Homophily: Structure-aware Path Aggregation Graph Neural NetworkCode1
RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive LearningCode1
Diffusion Improves Graph LearningCode1
Feature Expansion for Graph Neural NetworksCode1
Beyond Low-frequency Information in Graph Convolutional NetworksCode1
DiffWire: Inductive Graph Rewiring via the Lovász BoundCode1
An Empirical Study of Graph Contrastive LearningCode1
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-LearningCode1
Self-supervised edge features for improved Graph Neural Network trainingCode1
Self-Supervised Node Representation Learning via Node-to-Neighbourhood AlignmentCode1
A New Graph Node Classification Benchmark: Learning Structure from Histology Cell GraphsCode1
Geom-GCN: Geometric Graph Convolutional NetworksCode1
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional NetworksCode1
Should Graph Convolution Trust Neighbors? A Simple Causal Inference MethodCode1
SimMatchV2: Semi-Supervised Learning with Graph ConsistencyCode1
Directional Graph NetworksCode1
Multi-hop Attention Graph Neural NetworkCode1
Simple Spectral Graph ConvolutionCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Disease State Prediction From Single-Cell Data Using Graph Attention NetworksCode1
Disentangled Condensation for Large-scale GraphsCode1
S-Mixup: Structural Mixup for Graph Neural NetworksCode1
Finding Global Homophily in Graph Neural Networks When Meeting HeterophilyCode1
Spectral Augmentation for Self-Supervised Learning on GraphsCode1
Spectral Invariant Learning for Dynamic Graphs under Distribution ShiftsCode1
GRAF: Graph Attention-aware Fusion NetworksCode1
Distance-wise Prototypical Graph Neural Network in Node Imbalance ClassificationCode1
Robust Optimization as Data Augmentation for Large-scale GraphsCode1
Distilling Self-Knowledge From Contrastive Links to Classify Graph Nodes Without Passing MessagesCode1
Bilinear Graph Neural Network with Neighbor InteractionsCode1
Graph Convolutional Networks for Graphs Containing Missing FeaturesCode1
Graph Ordering Attention NetworksCode1
TAM: Topology-Aware Margin Loss for Class-Imbalanced Node ClassificationCode1
Task-Adaptive Few-shot Node ClassificationCode1
Lifelong Learning of Graph Neural Networks for Open-World Node ClassificationCode1
Open-World Semi-Supervised Learning for Node ClassificationCode1
Accelerating Large Scale Real-Time GNN Inference using Channel PruningCode1
When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node DistinguishabilityCode1
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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