Understanding Pooling in Graph Neural Networks.
IEEE Trans Neural Netw Learn Syst
; PP2022 Jul 21.
Article
em En
| MEDLINE
| ID: mdl-35862329
ABSTRACT
Many recent works in the field of graph machine learning have introduced pooling operators to reduce the size of graphs. In this article, we present an operational framework to unify this vast and diverse literature by describing pooling operators as the combination of three functions selection, reduction, and connection (SRC). We then introduce a taxonomy of pooling operators, based on some of their key characteristics and implementation differences under the SRC framework. Finally, we propose three criteria to evaluate the performance of pooling operators and use them to investigate the behavior of different operators on a variety of tasks.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
IEEE Trans Neural Netw Learn Syst
Ano de publicação:
2022
Tipo de documento:
Article