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

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Showing 1301–1350 of 1860 papers

TitleStatusHype
GCN-SL: Graph Convolutional Network with Structure Learning for Disassortative Graphs—0
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling—0
Generating Human Understandable Explanations for Node Embeddings—0
Generating Post-hoc Explanations for Skip-gram-based Node Embeddings by Identifying Important Nodes with Bridgeness—0
Generating Robust Counterfactual Witnesses for Graph Neural Networks—0
Generation is better than Modification: Combating High Class Homophily Variance in Graph Anomaly Detection—0
Generative and Contrastive Paradigms Are Complementary for Graph Self-Supervised Learning—0
GENIE: Watermarking Graph Neural Networks for Link Prediction—0
Geometric Graph Representation Learning via Maximizing Rate Reduction—0
Geometric Multimodal Deep Learning with Multi-Scaled Graph Wavelet Convolutional Network—0
Geometric Pooling: maintaining more useful information—0
Global-Local Graph Neural Networks for Node-Classification—0
Global Node Attentions via Adaptive Spectral Filters—0
GNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks—0
GNNDLD: Graph Neural Network with Directional Label Distribution—0
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels—0
GNN-MultiFix: Addressing the pitfalls for GNNs for multi-label node classification—0
GNNLens: A Visual Analytics Approach for Prediction Error Diagnosis of Graph Neural Networks—0
Gophormer: Ego-Graph Transformer for Node Classification—0
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily—0
Gradient Inversion Attack on Graph Neural Networks—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Graph Agent: Explicit Reasoning Agent for Graphs—0
Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience—0
Graph Agreement Models for Semi-Supervised Learning—0
Graph Anomaly Detection with Graph Neural Networks: Current Status and Challenges—0
Graph Attention Networks with Positional Embeddings—0
Graph Augmentation for Cross Graph Domain Generalization—0
Graph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling—0
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better—0
Class-Balanced and Reinforced Active Learning on Graphs—0
GraphCL: Contrastive Self-Supervised Learning of Graph Representations—0
Graph Clustering with Graph Neural Networks—0
Graph CNN for Moving Object Detection in Complex Environments from Unseen Videos—0
Graph Coarsening with Message-Passing Guarantees—0
Graph Convolution: A High-Order and Adaptive Approach—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph Decipher: A transparent dual-attention graph neural network to understand the message-passing mechanism for the node classification—0
Graph Decoupling Attention Markov Networks for Semi-supervised Graph Node Classification—0
Each Graph is a New Language: Graph Learning with LLMs—0
Graph Embedding with Hierarchical Attentive Membership—0
Graph Embedding with Rich Information through Heterogeneous Network—0
GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification—0
Graph-FCN for image semantic segmentation—0
GraphFL: A Federated Learning Framework for Semi-Supervised Node Classification on Graphs—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
GraphGAN: Generating Graphs via Random Walks—0
GISExplainer: On Explainability of Graph Neural Networks via Game-theoretic Interaction Subgraphs—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
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