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 521–530 of 982 papers

TitleStatusHype
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
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Benchmark Results

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