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 501–550 of 1860 papers

TitleStatusHype
Node Classification in Random TreesCode0
Unveiling the Unseen Potential of Graph Learning through MLPs: Effective Graph Learners Using Propagation-Embracing MLPs—0
Self-Supervised Pretraining for Heterogeneous Hypergraph Neural Networks—0
Improvements on Uncertainty Quantification for Node Classification via Distance-Based RegularizationCode0
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation—0
Mixture of Weak & Strong Experts on GraphsCode1
Predicting Properties of Nodes via Community-Aware FeaturesCode0
Edge2Node: Reducing Edge Prediction to Node Classification—0
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 Classification—0
Hyperbolic Graph Neural Networks at Scale: A Meta Learning Approach—0
Rethinking Semi-Supervised Imbalanced Node Classification from Bias-Variance DecompositionCode1
BLIS-Net: Classifying and Analyzing Signals on Graphs—0
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural NetworksCode0
Graph Agent: Explicit Reasoning Agent for Graphs—0
Resurrecting Label Propagation for Graphs with Heterophily and Label NoiseCode0
Graph Neural Networks with a Distribution of Parametrized Graphs—0
Hierarchical Randomized Smoothing—0
Deceptive Fairness Attacks on Graphs via Meta LearningCode0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks—0
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels—0
A Study on Knowledge Graph Embeddings and Graph Neural Networks for Web Of ThingsCode0
Fairness-aware Optimal Graph Filter Design—0
Neighborhood Homophily-Guided Graph Convolutional NetworkCode0
SplitGNN: Spectral Graph Neural Network for Fraud Detection against HeterophilyCode1
Positive-Unlabeled Node Classification with Structure-aware Graph Learning—0
Pretraining Language Models with Text-Attributed Heterogeneous GraphsCode1
Exploring Graph Neural Networks for Indian Legal Judgment Prediction—0
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale—0
Hetero^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs—0
Privacy-Preserving Graph Embedding based on Local Differential PrivacyCode0
A Local Graph Limits Perspective on Sampling-Based GNNs—0
SignGT: Signed Attention-based Graph Transformer for Graph Representation Learning—0
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 Representation—0
Causality and Independence Enhancement for Biased Node ClassificationCode0
Heterophily-Based Graph Neural Network for Imbalanced Classification—0
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
1NodeNetAccuracy80.09—Unverified
2SplineCNNAccuracy79.2—Unverified
3PathNetAccuracy (%)77.98—Unverified
43ferenceAccuracy76.33—Unverified
5MMAAccuracy76.3—Unverified
6PPNPAccuracy75.83—Unverified
7CoLinkDistAccuracy75.79—Unverified
8CoLinkDistMLPAccuracy75.77—Unverified
9APPNPAccuracy75.73—Unverified
10CleoraAccuracy75.7—Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21—Unverified
2CoLinkDistAccuracy89.58—Unverified
3CoLinkDistMLPAccuracy89.53—Unverified
4PathNetAccuracy (%)88.92—Unverified
53ferenceAccuracy88.9—Unverified
6SplineCNNAccuracy88.88—Unverified
7LinkDistAccuracy88.86—Unverified
8LinkDistMLPAccuracy88.79—Unverified
9PairEF188.57—Unverified
10GCN + MixupAccuracy87.9—Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24—Unverified
2CoLinkDistAccuracy87.89—Unverified
33ferenceAccuracy87.78—Unverified
4LinkDistMLPAccuracy87.58—Unverified
5CoLinkDistMLPAccuracy87.54—Unverified
6CleoraAccuracy86.8—Unverified
7NodeNetAccuracy86.8—Unverified
8MMAAccuracy85.8—Unverified
9GResNet(GAT)Accuracy85.5—Unverified
10DifNetAccuracy85.1—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5—Unverified
2LDS-GNNAccuracy75—Unverified
3CPF-tra-APPNPAccuracy74.6—Unverified
4G3NNAccuracy74.5—Unverified
5GEMAccuracy74.2—Unverified
6GGCMAccuracy74.2—Unverified
7Truncated KrylovAccuracy73.86—Unverified
8SSGCAccuracy73.6—Unverified
9OKDEEMAccuracy73.53—Unverified
10GCNIIAccuracy73.4—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4—Unverified
2CPF-tra-GCNIIAccuracy83.2—Unverified
3DSGCNAccuracy81.9—Unverified
4Truncated KrylovAccuracy81.7—Unverified
5SuperGAT MXAccuracy81.7—Unverified
6G-APPNPAccuracy80.95—Unverified
7GGCMAccuracy80.8—Unverified
8GCN(predicted-targets)Accuracy80.42—Unverified
9SSGCAccuracy80.4—Unverified
10GCNIIAccuracy80.2—Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9—Unverified
2GCN-TVAccuracy86.3—Unverified
3GCNIIAccuracy85.5—Unverified
4CPF-ind-APPNPAccuracy85.3—Unverified
5AIR-GCNAccuracy84.7—Unverified
6H-GCNAccuracy84.5—Unverified
7G-APPNPAccuracy84.31—Unverified
8SuperGAT MXAccuracy84.3—Unverified
9DSGCNAccuracy84.2—Unverified
10LDS-GNNAccuracy84.1—Unverified