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 581–590 of 982 papers

TitleStatusHype
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
Hierarchical Graph Representation Learning for the Prediction of Drug-Target Binding AffinityCode1
Explainability in Graph Neural Networks: An Experimental Survey—0
Few-Shot Learning on Graphs—0
Graph Representation Learning with Individualization and Refinement—0
Graph Representation Learning for Popularity Prediction Problem: A Survey—0
Multi-modal Graph Learning for Disease PredictionCode1
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
Graph Representation Learning Beyond Node and HomophilyCode0
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Benchmark Results

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