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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 391–400 of 982 papers

TitleStatusHype
Accurate and Definite Mutational Effect Prediction with Lightweight Equivariant Graph Neural Networks—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
A Comprehensive Survey on Deep Graph Representation Learning—0
CAFIN: Centrality Aware Fairness inducing IN-processing for Unsupervised Representation Learning on GraphsCode0
Class-Imbalanced Learning on Graphs: A SurveyCode1
Graph Representation Learning for Interactive Biomolecule Systems—0
Attribute-Consistent Knowledge Graph Representation Learning for Multi-Modal Entity Alignment—0
FMGNN: Fused Manifold Graph Neural Network—0
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
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Benchmark Results

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