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 1551–1600 of 1860 papers

TitleStatusHype
Minimal Driver Nodes for Structural Controllability of Large-Scale Dynamical Systems: Node Classification—0
Missing Data Estimation in Temporal Multilayer Position-aware Graph Neural Network (TMP-GNN)—0
Mitigating Degree Bias Adaptively with Hard-to-Learn Nodes in Graph Contrastive Learning—0
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Mixture of Decoupled Message Passing Experts with Entropy Constraint for General Node Classification—0
Mixture of Experts for Node Classification—0
m-mix: Generating hard negatives via multiple samples mixing for contrastive learning—0
Modeling Graph Node Correlations with Neighbor Mixture Models—0
Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity—0
motif2vec: Motif Aware Node Representation Learning for Heterogeneous Networks—0
Multi-duplicated Characterization of Graph Structures using Information Gain Ratio for Graph Neural Networks—0
Multi-frame Detection via Graph Neural Networks: A Link Prediction Approach—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
Multi-Label Graph Convolutional Network Representation Learning—0
MULTI-LEVEL APPROACH TO ACCURATE AND SCALABLE HYPERGRAPH EMBEDDING—0
Multi-Relational Classification via Bayesian Ranked Non-Linear Embeddings—0
Multi-scale Graph Convolutional Networks with Self-Attention—0
Multi-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling—0
Multi-task Self-distillation for Graph-based Semi-Supervised Learning—0
Multivariate Relations Aggregation Learning in Social Networks—0
Multi-view graph structure learning using subspace merging on Grassmann manifold—0
Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Data—0
MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale—0
MUSE: Multi-View Contrastive Learning for Heterophilic Graphs—0
NCGNN: Node-Level Capsule Graph Neural Network for Semisupervised Classification—0
NDGGNET-A Node Independent Gate based Graph Neural Networks—0
Neighbor2vec: an efficient and effective method for Graph Embedding—0
Neighbor Enhanced Graph Convolutional Networks for Node Classification and Recommendation—0
Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification—0
Neighborhood Random Walk Graph Sampling for Regularized Bayesian Graph Convolutional Neural Networks—0
NEMR: Network Embedding on Metric of Relation—0
Scalable Hypergraph Embedding System—0
Network2Vec Learning Node Representation Based on Space Mapping in Networks—0
Network In Graph Neural Network—0
Network Lens: Node Classification in Topologically Heterogeneous Networks—0
Network of Graph Convolutional Networks Trained on Random Walks—0
Network Vector: Distributed Representations of Networks with Global Context—0
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network—0
Neurally boosted supervised spectral clustering—0
Neural Models for Output-Space Invariance in Combinatorial Problems—0
Neural Networks in a Product of Hyperbolic Spaces—0
Neural Trees for Learning on Graphs—0
Next Waves in Veridical Network Embedding—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks—0
Node Classification and Search on the Rubik's Cube Graph with GNNs—0
Node Classification in Uncertain Graphs—0
Node Classification Meets Link Prediction on Knowledge Graphs—0
Node Classification via Semantic-Structural Attention-Enhanced Graph Convolutional Networks—0
Node Classification With Integrated Reject Option—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