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

Recommender systems are ubiquitous in most of our interactions in the current
digital world. Whether shopping for clothes, scrolling YouTube for exciting
videos, or searching for restaurants in a new city, the recommender systems at
the back-end power these services. Most large-scale recommender systems are
huge models trained on extensive datasets and are black-boxes to both their
developers and end-users. Prior research has shown that providing
recommendations along with their reason enhances trust, scrutability, and
persuasiveness of the recommender systems. Recent literature in explainability
has been inundated with works proposing several algorithms to this end. Most of
these works provide item-style explanations, i.e., `We recommend item A because
you bought item B.' We propose a novel approach, RecXplainer, to generate more
fine-grained explanations based on the user's preference over the attributes of
the recommended items. We perform experiments using real-world datasets and
demonstrate the efficacy of RecXplainer in capturing users' preferences and
using them to explain recommendations. We also propose ten new evaluation
metrics and compare RecXplainer to six baseline methods.

本文提出了一种新方法 RecXplainer，基于用户对推荐项目属性的偏好生成了更细粒度的推荐解释，并使用真实数据集进行了实验进行了评估，结果表明 RecXplainer 能够更好地捕捉用户的喜好，并且相比六种基线方法，RecXplainer 在提供推荐解释方面有更显著的效果。此外，作者还提出了十种新的推荐解释评估度量标准。