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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–800 of 1860 papers

TitleStatusHype
Graph Embedding with Hierarchical Attentive Membership—0
Graph Embedding with Rich Information through Heterogeneous Network—0
CPT: Competence-progressive Training Strategy for Few-shot Node Classification—0
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification—0
Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance—0
Bandit Sampling for Multiplex Networks—0
Deep Partial Multiplex Network Embedding—0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—0
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing—0
GraphFM: A Scalable Framework for Multi-Graph Pretraining—0
GraphFM: Improving Large-Scale GNN Training via Feature Momentum—0
Graphfool: Targeted Label Adversarial Attack on Graph Embedding—0
Effective backdoor attack on graph neural networks in link prediction tasks—0
Geometric Pooling: maintaining more useful information—0
GraphGAN: Generating Graphs via Random Walks—0
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks—0
Hierarchical Compression of Text-Rich Graphs via Large Language Models—0
Hierarchical Model Selection for Graph Neural Netoworks—0
Relational Graph Neural Network Design via Progressive Neural Architecture Search—0
Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network—0
Label Efficient Regularization and Propagation for Graph Node Classification—0
HetFS: A Method for Fast Similarity Search with Ad-hoc Meta-paths on Heterogeneous Information Networks—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Graph Inference Learning for Semi-supervised Classification—0
Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification—0
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data—0
Graph Information Matters: Understanding Graph Filters from Interaction Probability—0
GraphiT: Efficient Node Classification on Text-Attributed Graphs with Prompt Optimized LLMs—0
Adaptive Data Augmentation on Temporal Graphs—0
HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks—0
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs—0
Graph Convolutional Network For Semi-supervised Node Classification With Subgraph Sketching—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Heterophily-Based Graph Neural Network for Imbalanced Classification—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
Heterophily-Aware Graph Attention Network—0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Graph Masked Language Models—0
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks—0
Graph Mining under Data scarcity—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
Convolutional Signal Propagation: A Simple Scalable Algorithm for Hypergraphs—0
Graph Neural Aggregation-diffusion with Metastability—0
Generating Robust Counterfactual Witnesses for Graph Neural Networks—0
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness—0
Graph Neural Networks at a Fraction—0
A Complex Network based Graph Embedding Method for Link Prediction—0
Generating Human Understandable Explanations for Node Embeddings—0
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All datasetsCiteseerPubmedCoraCiteSeer with Public Split: fixed 20 nodes per classPubMed with Public Split: fixed 20 nodes per classCora with Public Split: fixed 20 nodes per classPPICoauthor CSCora (0.5%)Cora (1%)Cora (3%)PubMed (0.03%)

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
10TransGNN1:1 Accuracy85.1—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