Survey on Trustworthy Graph Neural Networks: From A Causal Perspective
Wenzhao Jiang Hao Liu Hui Xiong
Graph Neural Networks (GNNs) have emerged as powerful representation learning
tools for capturing complex dependencies within diverse graph-structured data.
Despite their success in a wide range of graph mining tasks, GNNs have raised
serious concerns regarding their trustworthiness, including susceptibility to
distribution shift, biases towards certain populations, and lack of
explainability. Recently, integrating causal learning techniques into GNNs has
sparked numerous ground-breaking studies since most of the trustworthiness
issues can be alleviated by capturing the underlying data causality rather than
superficial correlations. In this survey, we provide a comprehensive review of
recent research efforts on causality-inspired GNNs. Specifically, we first
present the key trustworthy risks of existing GNN models through the lens of
causality. Moreover, we introduce a taxonomy of Causality-Inspired GNNs
(CIGNNs) based on the type of causal learning capability they are equipped
with, i.e., causal reasoning and causal representation learning. Besides, we
systematically discuss typical methods within each category and demonstrate how
they mitigate trustworthiness risks. Finally, we summarize useful resources and
discuss several future directions, hoping to shed light on new research
opportunities in this emerging field. The representative papers, along with
open-source data and codes, are available in
https://github.com/usail-hkust/Causality-Inspired-GNNs.