GCI: A (G)raph (C)oncept (I)nterpretation Framework. (arXiv:2302.04899v1 [cs.LG])

Explainable AI (XAI) underwent a recent surge in research on concept
extraction, focusing on extracting human-interpretable concepts from Deep
Neural Networks. An important challenge facing concept extraction approaches is
the difficulty of interpreting and evaluating discovered concepts, especially
for complex tasks such as molecular property prediction. We address this
challenge by presenting GCI: a (G)raph (C)oncept (I)nterpretation framework,
used for quantitatively measuring alignment between concepts discovered from
Graph Neural Networks (GNNs) and their corresponding human interpretations. GCI
encodes concept interpretations as functions, which can be used to
quantitatively measure the alignment between a given interpretation and concept
definition. We demonstrate four applications of GCI: (i) quantitatively
evaluating concept extractors, (ii) measuring alignment between concept
extractors and human interpretations, (iii) measuring the completeness of
interpretations with respect to an end task and (iv) a practical application of
GCI to molecular property prediction, in which we demonstrate how to use
chemical functional groups to explain GNNs trained on molecular property
prediction tasks, and implement interpretations with a 0.76 AUCROC completeness
score.