Robust Ante-hoc Graph Explainer using Bilevel Optimization
Mert Kosan Arlei Silva Ambuj Singh
Explaining the decisions made by machine learning models for high-stakes
applications is critical for increasing transparency and guiding improvements
to these decisions. This is particularly true in the case of models for graphs,
where decisions often depend on complex patterns combining rich structural and
attribute data. While recent work has focused on designing so-called post-hoc
explainers, the question of what constitutes a good explanation remains open.
One intuitive property is that explanations should be sufficiently informative
to enable humans to approximately reproduce the predictions given the data.
However, we show that post-hoc explanations do not achieve this goal as their
explanations are highly dependent on fixed model parameters (e.g., learned GNN
weights). To address this challenge, this paper proposes RAGE (Robust Ante-hoc
Graph Explainer), a novel and flexible ante-hoc explainer designed to discover
explanations for a broad class of graph neural networks using bilevel
optimization. RAGE is able to efficiently identify explanations that contain
the full information needed for prediction while still enabling humans to rank
these explanations based on their influence. Our experiments, based on graph
classification and regression, show that RAGE explanations are more robust than
existing post-hoc and ante-hoc approaches and often achieve similar or better
accuracy than state-of-the-art models.