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Chapter and Conference Paper
Splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression
This paper introduces ReGenGraph, a new method for graph regression that combines two well-known modules: an autoencoder and a graph autoencoder. The main objective of our proposal is to split the knowledge in...
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Chapter and Conference Paper
Graph Regression Based on Graph Autoencoders
We offer in this paper a trial of encoding graph data as means of efficient prediction in a parallel setup. The first step converts graph data into feature vectors through a Graph Autoencoder (G-AE). Then, der...