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 681–690 of 982 papers

TitleStatusHype
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Using Constraint Programming and Graph Representation Learning for Generating Interpretable Cloud Security PoliciesCode0
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
A Hierarchical Block Distance Model for Ultra Low-Dimensional Graph RepresentationsCode0
A Survey on Graph Representation Learning Methods—0
On Understanding and Mitigating the Dimensional Collapse of Graph Contrastive Learning: a Non-Maximum Removal Approach—0
Explainability in Graph Neural Networks: An Experimental Survey—0
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Benchmark Results

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