SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 501–550 of 982 papers

TitleStatusHype
Wasserstein Hypergraph Neural Network—0
XLVIN: eXecuted Latent Value Iteration Nets—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
A Causal Disentangled Multi-Granularity Graph Classification Method—0
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Accurate Text-Enhanced Knowledge Graph Representation Learning—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
A Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers—0
A Comprehensive Analytical Survey on Unsupervised and Semi-Supervised Graph Representation Learning Methods—0
A Comprehensive Survey on Deep Graph Representation Learning—0
A Conjoint Graph Representation Learning Framework for Hypertension Comorbidity Risk Prediction—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Adaptive Multi-Neighborhood Attention based Transformer for Graph Representation Learning—0
A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base—0
A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail—0
A Deep Latent Space Model for Directed Graph Representation Learning—0
DPGNN: Dual-Perception Graph Neural Network for Representation Learning—0
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions—0
Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of Techniques—0
Adversarial Attack on Hierarchical Graph Pooling Neural Networks—0
Adversarial Classifier for Imbalanced Problems—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
A General-Purpose Transferable Predictor for Neural Architecture Search—0
Graph Neural Network-based Spectral Filtering Mechanism for Imbalance Classification in Network Digital Twin—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
AMinerGNN: Heterogeneous Graph Neural Network for Paper Click-through Rate Prediction with Fusion Query—0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems—0
Application of Graph Neural Networks and graph descriptors for graph classification—0
Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?—0
Are Hyperbolic Representations in Graphs Created Equal?—0
A Scalable and Effective Alternative to Graph Transformers—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
A Self-supervised Mixed-curvature Graph Neural Network—0
A Self-supervised Riemannian GNN with Time Varying Curvature for Temporal Graph Learning—0
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective—0
A Survey on Graph Representation Learning Methods—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified