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 1051–1100 of 1860 papers

TitleStatusHype
A Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks—0
A semantic backdoor attack against Graph Convolutional Networks—0
ASGNN: Graph Neural Networks with Adaptive Structure—0
A Simple Hypergraph Kernel Convolution based on Discounted Markov Diffusion Process—0
Supervised Graph Contrastive Learning for Few-shot Node Classification—0
Associative Learning for Network Embedding—0
A Survey on Embedding Dynamic Graphs—0
A Survey on Graph Classification and Link Prediction based on GNN—0
A Survey on Graph Representation Learning Methods—0
A Survey on Signed Graph Embedding: Methods and Applications—0
ASWT-SGNN: Adaptive Spectral Wavelet Transform-based Self-Supervised Graph Neural Network—0
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids—0
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks—0
Attacking Graph Convolutional Networks via Rewiring—0
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
Attributed Graph Learning with 2-D Graph Convolution—0
Attributed Network Embedding for Learning in a Dynamic Environment—0
A Tutorial on Online Supervised Learning with Applications to Node Classification in Social Networks—0
A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy—0
Auto-GNN: Neural Architecture Search of Graph Neural Networks—0
AutoGraph: Automated Graph Neural Network—0
Automated Graph Learning via Population Based Self-Tuning GCN—0
Automated Knowledge Modeling for Cancer Clinical Practice Guidelines—0
Automated Loss function Search for Class-imbalanced Node Classification—0
Auxiliary learning induced graph convolutional networks—0
A Vertical Federated Learning Framework for Graph Convolutional Network—0
Bandit Sampling for Multiplex Networks—0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
Batch Virtual Adversarial Training for Graph Convolutional Networks—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
Bayesian Graph Convolutional Neural Networks using Node Copying—0
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks—0
Better Schedules for Low Precision Training of Deep Neural Networks—0
Beyond Homophily with Graph Echo State Networks—0
Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs—0
Beyond Node Attention: Multi-Scale Harmonic Encoding for Feature-Wise Graph Message Passing—0
Beyond the Known: Novel Class Discovery for Open-world Graph Learning—0
BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
BiGCN: A Bi-directional Low-Pass Filtering Graph Neural Network—0
Bi-level Multi-objective Evolutionary Learning: A Case Study on Multi-task Graph Neural Topology Search—0
Label Inference Attacks against Node-level Vertical Federated GNNs—0
BLIS-Net: Classifying and Analyzing Signals on Graphs—0
Blockchain Phishing Scam Detection via Multi-channel Graph Classification—0
Bonsai: Gradient-free Graph Condensation for Node Classification—0
Boosting Graph Neural Networks via Adaptive Knowledge Distillation—0
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification—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
6NodeNetAccuracy86.8—Unverified
7CleoraAccuracy86.8—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
5GGCMAccuracy74.2—Unverified
6GEMAccuracy74.2—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
4SuperGAT MXAccuracy81.7—Unverified
5Truncated KrylovAccuracy81.7—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