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.

提供 HELP (Hierarchical Explainable Latent Pooling) 这一新颖、本质上可解释的图汇聚方法，它揭示了不同图神经网络层次的概念如何组合成后续步骤中的新概念。通过定性分析和定量验证，我们证明了 HELP 在化学和社交网络领域的领域知识上与标准 GCN 和热门汇聚方法表现相当的准确性，同时提供了更易于理解的解释。这项工作代表了对图神经网络的理解迈出的第一步，超越了来自最终层次的一组概念，而是解释了不同层次概念之间的复杂相互作用。