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 201–225 of 1860 papers

TitleStatusHype
Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs—0
Leiden-Fusion Partitioning Method for Effective Distributed Training of Graph Embeddings—0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
CliquePH: Higher-Order Information for Graph Neural Networks through Persistent Homology on Clique GraphsCode0
Virtual Node Generation for Node Classification in Sparsely-Labeled Graphs—0
Information Extraction from Visually Rich Documents Using Directed Weighted Graph Neural NetworkCode0
Mixture of Scope Experts at Test: Generalizing Deeper Graph Neural Networks with Shallow VariantsCode0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Generalized Learning of Coefficients in Spectral Graph Convolutional NetworksCode0
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior NetworksCode0
A Survey on Signed Graph Embedding: Methods and Applications—0
Self-Directed Learning of Convex Labelings on Graphs—0
Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graph—0
Reproducibility Study Of Learning Fair Graph Representations Via Automated Data AugmentationsCode0
GSTAM: Efficient Graph Distillation with Structural Attention-MatchingCode0
SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classificationCode0
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks—0
Exploring the Potential of Large Language Models for Heterophilic Graphs—0
LLMs as Zero-shot Graph Learners: Alignment of GNN Representations with LLM Token EmbeddingsCode2
RoCP-GNN: Robust Conformal Prediction for Graph Neural Networks in Node-Classification—0
Slicing Input Features to Accelerate Deep Learning: A Case Study with Graph Neural Networks—0
Federated Graph Learning with Structure Proxy AlignmentCode0
Leveraging Invariant Principle for Heterophilic Graph Structure Distribution Shifts—0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
Graph Triple Attention Network: A Decoupled PerspectiveCode0
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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
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
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