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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 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
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Benchmark Results

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