Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network

Recently, Their goal is to learn or search for optimal graph objects that heterogeneous graph neural networks (HGNs) have maximize mutual information with the predictions. While become one of the standard paradigms for modeling rich such explanations answer the question "what is salient to semantics of heterogeneous graphs in various application the prediction", they fail to unveil "how the salient objects domains such as e-commerce, finance, and healthcare (Lv affect the prediction". In particular, there may exist multiple et al. 2021; Wang et al. 2022). In parallel with the proliferation paths in the graph to propagate the information of the salient of HGNs, understanding the reasons behind the objects to the target object and affect its prediction. Without predictions from HGNs is urgently demanded in order to distinguishing these different influential paths, the answer to build trust and confidence in the models for both users and the "how" question remains unclear, which could compromise stakeholders. For example, a customer would be satisfied if the utility of the explanation. This issue becomes more an HGN-based recommender system accompanies recommended prominent when it comes to explaining HGNs due to the items with explanations; a bank manager may want complex semantics of heterogeneous graphs.