Interpretable graph neural networks (XGNNs ) are widely adopted in various
scientific applications involving graph-structured data. Existing XGNNs
predominantly adopt the attention-based mechanism to learn edge or node
importance for extracting and making predictions with the interpretable
subgraph. However, the representational properties and limitations of these
methods remain inadequately explored. In this work, we present a theoretical
framework that formulates interpretable subgraph learning with the multilinear
extension of the subgraph distribution, coined as subgraph multilinear
extension (SubMT). Extracting the desired interpretable subgraph requires an
accurate approximation of SubMT, yet we find that the existing XGNNs can have a
huge gap in fitting SubMT. Consequently, the SubMT approximation failure will
lead to the degenerated interpretability of the extracted subgraphs. To
mitigate the issue, we design a new XGNN architecture called Graph Multilinear
neT (GMT), which is provably more powerful in approximating SubMT. We
empirically validate our theoretical findings on a number of graph
classification benchmarks. The results demonstrate that GMT outperforms the
state-of-the-art up to 10% in terms of both interpretability and
generalizability across 12 regular and geometric graph benchmarks.

以图分类 benchmarks 为基础，提出了一种理论框架，通过 subgraph multilinear extension (SubMT) 来学习可解释图子图，设计了一种新的图神经网络架构（Graph Multilinear neT），在解释性和泛化性方面优于现有方法，提高了多项 benchmarks 上的表现。

可解释图神经网络的可解释性如何？

How Interpretable Are Interpretable Graph Neural Networks?

In this paper, we strive to develop an interpretable GNNs' inference
paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme
readily applicable to various GNNs' baselines. Unlike the most existing
explanation methods, MSInterpreter provides a Message-passing Selection
scheme(MSScheme) to select the critical paths for GNNs' message aggregations,
which aims at reaching the self-explaination instead of post-hoc explanations.
In detail, the elaborate MSScheme is designed to calculate weight factors of
message aggregation paths by considering the vanilla structure and node
embedding components, where the structure base aims at weight factors among
node-induced substructures; on the other hand, the node embedding base focuses
on weight factors via node embeddings obtained by one-layer GNN.Finally, we
demonstrate the effectiveness of our approach on graph classification
benchmarks.

本文介绍了一个名为 MSInterpreter 的可解释的 GNN 推理范例，提出了消息传递选择方案 (MSScheme) 来选定关键路径，旨在实现自解释而非事后解释，并在图分类基准测试中证明了方法的有效性。