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 501550 of 1860 papers

TitleStatusHype
Node Classification in Random TreesCode0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs0
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation0
Mixture of Weak & Strong Experts on GraphsCode1
Predicting Properties of Nodes via Community-Aware FeaturesCode0
Edge2Node: Reducing Edge Prediction to Node Classification0
Architecture Matters: Uncovering Implicit Mechanisms in Graph Contrastive LearningCode0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
Cooperative Network Learning for Large-Scale and Decentralized GraphsCode0
VIGraph: Generative Self-supervised Learning for Class-Imbalanced Node Classification0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach0
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance DecompositionCode1
BLIS-Net: Classifying and Analyzing Signals on Graphs0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Graph Agent: Explicit Reasoning Agent for Graphs0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Graph Neural Networks with a Distribution of Parametrized Graphs0
Hierarchical Randomized Smoothing0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning0
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks0
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels0
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of ThingsCode0
Fairness-aware Optimal Graph Filter Design0
Neighborhood Homophily-Guided Graph Convolutional NetworkCode0
SplitGNN: Spectral Graph Neural Network for Fraud Detection against HeterophilyCode1
Positive-Unlabeled Node Classification with Structure-aware Graph Learning0
Pretraining Language Models with Text-Attributed Heterogeneous GraphsCode1
Exploring Graph Neural Networks for Indian Legal Judgment Prediction0
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale0
Hetero^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs0
Privacy-Preserving Graph Embedding based on Local Differential PrivacyCode0
A Local Graph Limits Perspective on Sampling-Based GNNs0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning0
Leveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed GraphsCode1
Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation0
Causality and Independence Enhancement for Biased Node ClassificationCode0
Heterophily-Based Graph Neural Network for Imbalanced Classification0
Non-backtracking Graph Neural NetworksCode0
Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings AugmentationCode0
Tailoring Self-Attention for Graph via Rooted SubtreesCode1
Simple GNNs with Low Rank Non-parametric AggregatorsCode0
Label-free Node Classification on Graphs with Large Language Models (LLMS)Code1
GRAPES: Learning to Sample Graphs for Scalable Graph Neural NetworksCode1
HoloNets: Spectral Convolutions do extend to Directed GraphsCode0
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional NetworksCode0
Deep Insights into Noisy Pseudo Labeling on Graph DataCode0
The Map Equation Goes Neural: Mapping Network Flows with Graph Neural NetworksCode0
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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