a fair comparison of graph neural networks for graph classification

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. The authors tried to reproduce the results from such experiments to tackle the problem of ambiguity in experimental procedures and the impossibility of reproducing results. They also Standardized the experimental environment so that the results could be reproduced while using this environment.