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

Showing 14511500 of 1860 papers

TitleStatusHype
SAIL: Self-Augmented Graph Contrastive Learning0
Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence FunctionCode0
Lifelong Graph LearningCode1
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
Decoupled Variational Embedding for Signed Directed NetworksCode0
DVE: Dynamic Variational Embeddings with Applications in Recommender Systems0
Learning Node Representations against PerturbationsCode0
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approachCode1
Tackling Over-Smoothing for General Graph Convolutional Networks0
Optimization of Graph Neural Networks with Natural Gradient DescentCode1
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks0
GraphReach: Position-Aware Graph Neural Network using Reachability EstimationsCode0
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
Quaternion Graph Neural NetworksCode1
Degree-Quant: Quantization-Aware Training for Graph Neural Networks0
DINE: A Framework for Deep Incomplete Network Embedding0
Directed hypergraph neural network0
Multivariate Relations Aggregation Learning in Social Networks0
Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs0
node2coords: Graph Representation Learning with Wasserstein Barycenters0
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification0
PanRep: Graph neural networks for extracting universal node embeddings in heterogeneous graphsCode1
Second-Order Pooling for Graph Neural NetworksCode1
Fuzzy Graph Neural Network for Few-Shot LearningCode1
Adversarial Immunization for Certifiable Robustness on GraphsCode1
Towards Deeper Graph Neural NetworksCode1
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective0
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous GraphsCode1
GraphCL: Contrastive Self-Supervised Learning of Graph Representations0
Are Hyperbolic Representations in Graphs Created Equal?0
Deep Learning for Abstract Argumentation SemanticsCode1
Next Waves in Veridical Network Embedding0
Graph Convolutional Networks for Graphs Containing Missing FeaturesCode1
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation0
Pre-Trained Models for Heterogeneous Information Networks0
Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural NetworksCode1
Faster Graph Embeddings via CoarseningCode0
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Simple and Deep Graph Convolutional NetworksCode1
AEGCN: An Autoencoder-Constrained Graph Convolutional NetworkCode0
Scaling Graph Neural Networks with Approximate PageRankCode1
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation0
Graph Clustering with Graph Neural Networks0
Policy-GNN: Aggregation Optimization for Graph Neural NetworksCode0
Lifelong Learning of Graph Neural Networks for Open-World Node ClassificationCode1
Self-supervised edge features for improved Graph Neural Network trainingCode1
Non-Parametric Graph Learning for Bayesian Graph Neural Networks0
Graph Prototypical Networks for Few-shot Learning on Attributed NetworksCode1
Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy80.09Unverified
2SplineCNNAccuracy79.2Unverified
3PathNetAccuracy (%)77.98Unverified
43ferenceAccuracy76.33Unverified
5MMAAccuracy76.3Unverified
6PPNPAccuracy75.83Unverified
7CoLinkDistAccuracy75.79Unverified
8CoLinkDistMLPAccuracy75.77Unverified
9APPNPAccuracy75.73Unverified
10CleoraAccuracy75.7Unverified
#ModelMetricClaimedVerifiedStatus
1NodeNetAccuracy90.21Unverified
2CoLinkDistAccuracy89.58Unverified
3CoLinkDistMLPAccuracy89.53Unverified
4PathNetAccuracy (%)88.92Unverified
53ferenceAccuracy88.9Unverified
6SplineCNNAccuracy88.88Unverified
7LinkDistAccuracy88.86Unverified
8LinkDistMLPAccuracy88.79Unverified
9PairEF188.57Unverified
10GCN + MixupAccuracy87.9Unverified
#ModelMetricClaimedVerifiedStatus
1LinkDistAccuracy88.24Unverified
2CoLinkDistAccuracy87.89Unverified
33ferenceAccuracy87.78Unverified
4LinkDistMLPAccuracy87.58Unverified
5CoLinkDistMLPAccuracy87.54Unverified
6NodeNetAccuracy86.8Unverified
7CleoraAccuracy86.8Unverified
8MMAAccuracy85.8Unverified
9GResNet(GAT)Accuracy85.5Unverified
10TransGNN1:1 Accuracy85.1Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy77.5Unverified
2LDS-GNNAccuracy75Unverified
3CPF-tra-APPNPAccuracy74.6Unverified
4G3NNAccuracy74.5Unverified
5GEMAccuracy74.2Unverified
6GGCMAccuracy74.2Unverified
7Truncated KrylovAccuracy73.86Unverified
8SSGCAccuracy73.6Unverified
9OKDEEMAccuracy73.53Unverified
10GCNIIAccuracy73.4Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy83.4Unverified
2CPF-tra-GCNIIAccuracy83.2Unverified
3DSGCNAccuracy81.9Unverified
4Truncated KrylovAccuracy81.7Unverified
5SuperGAT MXAccuracy81.7Unverified
6G-APPNPAccuracy80.95Unverified
7GGCMAccuracy80.8Unverified
8GCN(predicted-targets)Accuracy80.42Unverified
9SSGCAccuracy80.4Unverified
10GCNIIAccuracy80.2Unverified
#ModelMetricClaimedVerifiedStatus
1OGCAccuracy86.9Unverified
2GCN-TVAccuracy86.3Unverified
3GCNIIAccuracy85.5Unverified
4CPF-ind-APPNPAccuracy85.3Unverified
5AIR-GCNAccuracy84.7Unverified
6H-GCNAccuracy84.5Unverified
7G-APPNPAccuracy84.31Unverified
8SuperGAT MXAccuracy84.3Unverified
9DSGCNAccuracy84.2Unverified
10LDS-GNNAccuracy84.1Unverified