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 851–900 of 1860 papers

TitleStatusHype
Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels—0
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
GANN: Graph Alignment Neural Network for Semi-Supervised Learning—0
Contrastive Disentangled Learning on Graph for Node Classification—0
GANExplainer: GAN-based Graph Neural Networks Explainer—0
Graph Transformers without Positional Encodings—0
Game-theoretic Counterfactual Explanation for Graph Neural Networks—0
ANAE: Learning Node Context Representation for Attributed Network Embedding—0
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank—0
Graph U-Net—0
Dirichlet Energy Enhancement of Graph Neural Networks by Framelet Augmentation—0
Graph View-Consistent Learning Network—0
Infant Cry Classification with Graph Convolutional Networks—0
Joint Learning of Hierarchical Community Structure and Node Representations: An Unsupervised Approach—0
Learning Robust Representation through Graph Adversarial Contrastive Learning—0
GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning over Large-scale Graphs—0
Continuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE—0
GaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck—0
Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs—0
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacks—0
GAGE: Geometry Preserving Attributed Graph Embeddings—0
ConTIG: Continuous Representation Learning on Temporal Interaction Graphs—0
G-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning—0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
Attend Who is Weak: Enhancing Graph Condensation via Cross-Free Adversarial Training—0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks—0
Constant Curvature Graph Convolutional Networks—0
Rethinking the Promotion Brought by Contrastive Learning to Semi-Supervised Node Classification—0
G1: Teaching LLMs to Reason on Graphs with Reinforcement Learning—0
A Collective Learning Framework to Boost GNN Expressiveness—0
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks—0
Attacking Graph Convolutional Networks via Rewiring—0
Adapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks—0
Hard Masking for Explaining Graph Neural Networks—0
ImGCL: Revisiting Graph Contrastive Learning on Imbalanced Node Classification—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation—0
HC-Ref: Hierarchical Constrained Refinement for Robust Adversarial Training of GNNs—0
From Spectral Graph Convolutions to Large Scale Graph Convolutional Networks—0
Distribution Consistency based Self-Training for Graph Neural Networks with Sparse Labels—0
Connecting Graph Convolution and Graph PCA—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
Hetero^2Net: Heterophily-aware Representation Learning on Heterogenerous Graphs—0
Document Network Projection in Pretrained Word Embedding Space—0
Connecting Graph Convolutional Networks and Graph-Regularized PCA—0
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks—0
Imbalanced Node Classification Beyond Homophilic Assumption—0
From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning—0
Conformal Inductive Graph Neural Networks—0
A Temporal Graph Neural Network for Cyber Attack Detection and Localization in Smart Grids—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
6NodeNetAccuracy86.8—Unverified
7CleoraAccuracy86.8—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