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 1151–1200 of 1860 papers

TitleStatusHype
Coupled Hierarchical Structure Learning using Tree-Wasserstein Distance—0
CPT: Competence-progressive Training Strategy for Few-shot Node Classification—0
Crossformer: Transformer with Alternated Cross-Layer Guidance—0
Crypto'Graph: Leveraging Privacy-Preserving Distributed Link Prediction for Robust Graph Learning—0
Curriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach—0
Curvature Graph Neural Network—0
Curve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning—0
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications—0
Cyclic Label Propagation for Graph Semi-supervised Learning—0
Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network—0
Data Augmentation for Graph Convolutional Network on Semi-Supervised Classification—0
Coresets for Estimating Means and Mean Square Error with Limited Greedy Samples—0
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs—0
DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification—0
Deep Attributed Network Representation Learning via Attribute Enhanced Neighborhood—0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Deep Hashing for Signed Social Network Embedding—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Deep Partial Multiplex Network Embedding—0
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing—0
Deep Semantic Graph Learning via LLM based Node Enhancement—0
DeGLIF for Label Noise Robust Node Classification using GNNs—0
Degree-Based Random Walk Approach for Graph Embedding—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs—0
Demystifying Graph Convolution with a Simple Concatenation—0
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification—0
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach—0
Detecting Topology Attacks against Graph Neural Networks—0
Determinate Node Selection for Semi-supervised Classification Oriented Graph Convolutional Networks—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Differentiable Meta Multigraph Search with Partial Message Propagation on Heterogeneous Information Networks—0
Diffusion Based Network Embedding—0
Diffusion Probabilistic Models for Structured Node Classification—0
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs—0
Dimensional Reweighting Graph Convolution Networks—0
DINE: A Framework for Deep Incomplete Network Embedding—0
DiP-GNN: Discriminative Pre-Training of Graph Neural Networks—0
Directed Homophily-Aware Graph Neural Network—0
Directed hypergraph neural network—0
Directed Semi-Simplicial Learning with Applications to Brain Activity Decoding—0
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation—0
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
Distributed Representation of Subgraphs—0
Distributional Signals for Node Classification in Graph Neural Networks—0
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels—0
Document Network Projection in Pretrained Word Embedding Space—0
Dual GNNs: Graph Neural Network Learning with Limited Supervision—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