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 691–700 of 982 papers

TitleStatusHype
Few-Shot Learning on Graphs—0
Graph Representation Learning with Individualization and Refinement—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Graph Representation Learning Beyond Node and HomophilyCode0
Understanding microbiome dynamics via interpretable graph representation learningCode0
Distribution Preserving Graph Representation Learning—0
Message passing all the way up—0
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning—0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
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Benchmark Results

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