Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Nearest Neighbours Graph Variational AutoEncoder

Lorenzo Arsini
* ORCID logo ,
Barbara Caccia
,
Andrea Ciardiello
,
Stefano Giagu
* ORCID logo ,
Carlo Mancini Terracciano
*
Version 1 : Received: 30 January 2023 / Approved: 2 February 2023 / Online: 2 February 2023 (03:36:17 CET)

A peer-reviewed article of this Preprint also exists.

Arsini, L.; Caccia, B.; Ciardiello, A.; Giagu, S.; Mancini Terracciano, C. Nearest Neighbours Graph Variational AutoEncoder. Algorithms 2023, 16, 143. Arsini, L.; Caccia, B.; Ciardiello, A.; Giagu, S.; Mancini Terracciano, C. Nearest Neighbours Graph Variational AutoEncoder. Algorithms 2023, 16, 143.

Abstract

We present a Deep Learning generative model specialized to work with graphs with a regular geometry. It is build on a Variational Autoencoder framework and employs Graph convolutional layers in both encoding and decoding phases. We also introduce a pooling technique (ReNN-Pool), used in the encoder, that allows to downsample graph nodes in a spatially uniform and highly interpretable way. In the decoder, a symmetrical un-pooling technique is used to retrieve the original dimensionality of graphs. Performance of the model are tested on the standard Sprite benchmark dataset, a set of 2D images of video game characters, adequately transforming images data into graphs, and on the more realistic use-case of a dataset of cylindrical-shaped graph data that describe the distributions of the energy deposited by a particle beam in a medium.

Keywords

Graph Neural Network; Variational Autoencoder; Pooling; Nearest Neighbours

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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