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

Showing 91100 of 982 papers

TitleStatusHype
Fast Graph Learning with Unique Optimal SolutionsCode1
Fast Graph Representation Learning with PyTorch GeometricCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
Audio Event-Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive LearningCode1
A Large-Scale Database for Graph Representation LearningCode1
Multi-hop Attention Graph Neural NetworkCode1
AutoGCL: Automated Graph Contrastive Learning via Learnable View GeneratorsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
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Benchmark Results

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