  Graph neural networks (GNNs) have led to major breakthroughs in a variety of
domains such as drug discovery, social network analysis, and travel time
estimation. However, they lack interpretability which hinders human trust and
thereby deployment to settings with high-stakes decisions. A line of
interpretable methods approach this by discovering a small set of relevant
concepts as subgraphs in the last GNN layer that together explain the
prediction. This can yield oversimplified explanations, failing to explain the
interaction between GNN layers. To address this oversight, we provide HELP
(Hierarchical Explainable Latent Pooling), a novel, inherently interpretable
graph pooling approach that reveals how concepts from different GNN layers
compose to new ones in later steps. HELP is more than 1-WL expressive and is
the first non-spectral, end-to-end-learnable, hierarchical graph pooling method
that can learn to pool a variable number of arbitrary connected components. We
empirically demonstrate that it performs on-par with standard GCNs and popular
pooling methods in terms of accuracy while yielding explanations that are
aligned with expert knowledge in the domains of chemistry and social networks.
In addition to a qualitative analysis, we employ concept completeness scores as
well as concept conformity, a novel metric to measure the noise in discovered
concepts, quantitatively verifying that the discovered concepts are
significantly easier to fully understand than those from previous work. Our
work represents a first step towards an understanding of graph neural networks
that goes beyond a set of concepts from the final layer and instead explains
the complex interplay of concepts on different levels.
