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 571–580 of 982 papers

TitleStatusHype
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security PoliciesCode0
An Effective and Efficient Entity Alignment Decoding Algorithm via Third-Order Tensor IsomorphismCode1
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
LiftPool: Lifting-based Graph Pooling for Hierarchical Graph Representation Learning—0
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
DropMessage: Unifying Random Dropping for Graph Neural NetworksCode1
Simplicial Attention NetworksCode1
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
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Benchmark Results

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