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 601–650 of 1860 papers

TitleStatusHype
Detecting Topology Attacks against Graph Neural Networks—0
Detecting Political Opinions in Tweets through Bipartite Graph Analysis: A Skip Aggregation Graph Convolution Approach—0
Graph Positional Autoencoders as Self-supervised Learners—0
Graph Neural Reaction Diffusion Models—0
Bayesian Graph Neural Network for Fast identification of critical nodes in Uncertain Complex Networks—0
Graph Neural Tangent Kernel: Convergence on Large Graphs—0
Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classification—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
Robust Graph Data Learning via Latent Graph Convolutional Representation—0
Demystifying Graph Convolution with a Simple Concatenation—0
Bayesian Graph Convolutional Neural Networks using Node Copying—0
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning—0
A Deep Graph Neural Networks Architecture Design: From Global Pyramid-like Shrinkage Skeleton to Local Topology Link Rewiring—0
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification—0
DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphs—0
Alternately Optimized Graph Neural Networks—0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
Degree-Quant: Quantization-Aware Training for Graph Neural Networks—0
A Magnetic Framelet-Based Convolutional Neural Network for Directed Graphs—0
Batch Virtual Adversarial Training for Graph Convolutional Networks—0
Degree-Based Random Walk Approach for Graph Embedding—0
Barlow Graph Auto-Encoder for Unsupervised Network Embedding—0
DeGLIF for Label Noise Robust Node Classification using GNNs—0
10,000+ Times Accelerated Robust Subset Selection (ARSS)—0
Graph Neural Networks with Feature and Structure Aware Random Walk—0
Graph Ordering: Towards the Optimal by Learning—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
A Local Graph Limits Perspective on Sampling-Based GNNs—0
Graph Neural Networks at a Fraction—0
Deep Semantic Graph Learning via LLM based Node Enhancement—0
DeepRicci: Self-supervised Graph Structure-Feature Co-Refinement for Alleviating Over-squashing—0
Bandits for Black-box Attacks to Graph Neural Networks with Structure Perturbation—0
Graph Neural Networks for Binary Programming—0
Deep Partial Multiplex Network Embedding—0
Bandit Sampling for Multiplex Networks—0
Graph Neural Aggregation-diffusion with Metastability—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
Deep Hashing for Signed Social Network Embedding—0
Graph Mining under Data scarcity—0
A Learned Generalized Geodesic Distance Function-Based Approach for Node Feature Augmentation on Graphs—0
Graph Memory Learning: Imitating Lifelong Remembering and Forgetting of Brain Networks—0
GraphMixup: Improving Class-Imbalanced Node Classification on Graphs by Self-supervised Context Prediction—0
A Vertical Federated Learning Framework for Graph Convolutional Network—0
A Comparative Study for Unsupervised Network Representation Learning—0
Auxiliary learning induced graph convolutional networks—0
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings—0
Graph Masked Language Models—0
Graph Learning for Anomaly Analytics: Algorithms, Applications, and Challenges—0
Deep Feature Learning of Multi-Network Topology for Node Classification—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