EiX-GNN : Concept-level eigencentrality explainer for graph neural networks
Résumé
Nowadays, deep prediction models, especially graph neural networks, have a major
place in critical applications. In such context, those models need to be highly
interpretable or being explainable by humans, and at the societal scope, this understanding
may also be feasible for humans that do not have a strong prior knowledge
in models and contexts that need to be explained. In the literature, explaining
is a human knowledge transfer process regarding a phenomenon between an explainer
and an explainee. We propose EiX-GNN (Eigencentrality eXplainer for
Graph Neural Networks) a new powerful method for explaining graph neural networks
that encodes computationally this social explainer-to-explainee dependence
underlying in the explanation process. To handle this dependency, we introduce
the notion of explainee concept assimibility which allows explainer to adapt its
explanation to explainee background or expectation. We lead a qualitative study
to illustrate our explainee concept assimibility notion on real-world data as well
as a qualitative study that compares, according to objective metrics established in
the literature, fairness and compactness of our method with respect to performing
state-of-the-art methods. It turns out that our method achieves strong results in
both aspects.
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