Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism. (arXiv:2201.12987v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2201.12987

Interpretable graph learning is in need as many scientific applications
depend on learning models to collect insights from graph-structured data.
Previous works mostly focused on using post-hoc approaches to interpret
pre-trained models (graph neural networks in particular). They argue against
inherently interpretable models because the good interpretability of these
models is often at the cost of their prediction accuracy. However, those
post-hoc methods often fail to provide stable interpretation and may extract
features that are spuriously correlated with the task. …

arxiv attention graph graph learning learning lg stochastic