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 1001–1050 of 1860 papers

TitleStatusHype
A clean-label graph backdoor attack method in node classification task—0
A Collective Learning Framework to Boost GNN Expressiveness—0
A Complex Network based Graph Embedding Method for Link Prediction—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
A Comparative Study for Unsupervised Network Representation Learning—0
A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Learning for Graphs with Noisy Structures—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity—0
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification—0
Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks—0
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank—0
Adaptive Data Augmentation on Temporal Graphs—0
A Deep Graph Neural Networks Architecture Design: From Global Pyramid-like Shrinkage Skeleton to Local Topology Link Rewiring—0
A Differential Geometric View and Explainability of GNN on Evolving Graphs—0
Feature Transportation Improves Graph Neural Networks—0
Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Adversarial Attacks on Deep Graph Matching—0
Adversarial Context Aware Network Embeddings for Textual Networks—0
Adversarial Network Embedding—0
A Federated Parameter Aggregation Method for Node Classification Tasks with Different Graph Network Structures—0
A Framework for Joint Unsupervised Learning of Cluster-Aware Embedding for Heterogeneous Networks—0
A General Framework for Content-enhanced Network Representation Learning—0
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
A Graph Convolutional Network Composition Framework for Semi-supervised Classification—0
A Graph Data Augmentation Strategy with Entropy Preservation—0
A graph similarity for deep learning—0
A Hard Label Black-box Adversarial Attack Against Graph Neural Networks—0
A Hierarchy of Graph Neural Networks Based on Learnable Local Features—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
A Hyperbolic-to-Hyperbolic Graph Convolutional Network—0
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings—0
A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
A Local Graph Limits Perspective on Sampling-Based GNNs—0
Alternately Optimized Graph Neural Networks—0
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Analysis of Corrected Graph Convolutions—0
Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory—0
Anisotropic Graph Convolutional Network for Semi-supervised Learning—0
A Non-negative Symmetric Encoder-Decoder Approach for Community Detection—0
AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack—0
A Process for the Evaluation of Node Embedding Methods in the Context of Node Classification—0
Are Hyperbolic Representations in Graphs Created Equal?—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