Heterogeneous graph neural networks (HGNs) are prominent approaches to node
classification tasks on heterogeneous graphs. Despite the superior performance,
insights about the predictions made from HGNs are obscure to humans. Existing
explainability techniques are mainly proposed for GNNs on homogeneous graphs.
They focus on highlighting salient graph objects to the predictions whereas the
problem of how these objects affect the predictions remains unsolved. Given
heterogeneous graphs with complex structures and rich semantics, it is
imperative that salient objects can be accompanied with their influence paths
to the predictions, unveiling the reasoning process of HGNs. In this paper, we
develop xPath, a new framework that provides fine-grained explanations for
black-box HGNs specifying a cause node with its influence path to the target
node. In xPath, we differentiate the influence of a node on the prediction
w.r.t. every individual influence path, and measure the influence by perturbing
graph structure via a novel graph rewiring algorithm. Furthermore, we introduce
a greedy search algorithm to find the most influential fine-grained
explanations efficiently. Empirical results on various HGNs and heterogeneous
graphs show that xPath yields faithful explanations efficiently, outperforming
the adaptations of advanced GNN explanation approaches.

本文提出了一种名为 xPath 的新框架，针对黑盒异质图神经网络提供细粒度的解释，包括因果节点与目标节点间的影响路径，通过新的图重连算法通过扰动图结构来测量节点对预测的影响，并引入贪婪搜索算法以高效地找到最具影响力的细粒度解释。实证结果表明，与先进的 GNN 解释方法相比，xPath 能够高效地产生准确解释。

面向异构图神经网络的细粒度可解释性研究

Towards Fine-Grained Explainability for Heterogeneous Graph Neural  Network

LSTM-based recurrent neural networks are the state-of-the-art for many
natural language processing (NLP) tasks. Despite their performance, it is
unclear whether, or how, LSTMs learn structural features of natural languages
such as subject-verb number agreement in English. Lacking this understanding,
the generality of LSTM performance on this task and their suitability for
related tasks remains uncertain. Further, errors cannot be properly attributed
to a lack of structural capability, training data omissions, or other
exceptional faults. We introduce *influence paths*, a causal account of
structural properties as carried by paths across gates and neurons of a
recurrent neural network. The approach refines the notion of influence (the
subject's grammatical number has influence on the grammatical number of the
subsequent verb) into a set of gate or neuron-level paths. The set localizes
and segments the concept (e.g., subject-verb agreement), its constituent
elements (e.g., the subject), and related or interfering elements (e.g.,
attractors). We exemplify the methodology on a widely-studied multi-layer LSTM
language model, demonstrating its accounting for subject-verb number agreement.
The results offer both a finer and a more complete view of an LSTM's handling
of this structural aspect of the English language than prior results based on
diagnostic classifiers and ablation.

本文引入了影响路径的概念，对 LSTM 网络中的语言结构特征进行了分析和研究，并精细化地定位和分段语言结构中的概念及其相关元素，从而更全面、准确地展现了 LSTM 网络处理英语语言结构方面的能力。