a fair comparison of graph neural networks for graph classification: Difference between revisions

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== Presented By ==
== Presented By ==
Jaskirat Singh Bhatia
Jaskirat Singh Bhatia
==Background==
Experimental reproducibility and replicability are critical topics in machine learning.
Authors have often raised concerns about their lack in scientific publications
to improve the quality of the field. Recently, the graph representation learning
field has attracted the attention of a wide research community, which resulted in
a large stream of works. As such, several Graph Neural Network models have
been developed to effectively tackle graph classification. However, experimental
procedures often lack rigorousness and are hardly reproducible.

Revision as of 17:33, 9 November 2020

Presented By

Jaskirat Singh Bhatia

Background

Experimental reproducibility and replicability are critical topics in machine learning. Authors have often raised concerns about their lack in scientific publications to improve the quality of the field. Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works. As such, several Graph Neural Network models have been developed to effectively tackle graph classification. However, experimental procedures often lack rigorousness and are hardly reproducible.