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 221–230 of 982 papers

TitleStatusHype
Disentangle-based Continual Graph Representation LearningCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
GRPE: Relative Positional Encoding for Graph TransformerCode1
Neural Approximation of Graph Topological FeaturesCode1
Bi-GCN: Binary Graph Convolutional NetworkCode1
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Enhancing Graph Representation Learning with Localized Topological FeaturesCode1
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
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Benchmark Results

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