  While graph neural networks (GNNs) have been shown to perform well on
graph-based data from a variety of fields, they suffer from a lack of
transparency and accountability, which hinders trust and consequently the
deployment of such models in high-stake and safety-critical scenarios. Even
though recent research has investigated methods for explaining GNNs, these
methods are limited to single-instance explanations, also known as local
explanations. Motivated by the aim of providing global explanations, we adapt
the well-known Automated Concept-based Explanation approach (Ghorbani et al.,
2019) to GNN node and graph classification, and propose GCExplainer.
GCExplainer is an unsupervised approach for post-hoc discovery and extraction
of global concept-based explanations for GNNs, which puts the human in the
loop. We demonstrate the success of our technique on five node classification
datasets and two graph classification datasets, showing that we are able to
discover and extract high-quality concept representations by putting the human
in the loop. We achieve a maximum completeness score of 1 and an average
completeness score of 0.753 across the datasets. Finally, we show that the
concept-based explanations provide an improved insight into the datasets and
GNN models compared to the state-of-the-art explanations produced by
GNNExplainer (Ying et al., 2019).
