When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
图神经网络与因果学习：机遇、方法与展望AI 解读 ↓
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图神经网络与因果学习：机遇、方法与展望
- 研究问题
- 因果学习如何帮助理解图神经网络在分布变化、群体偏差和可解释性方面的可信度问题，现有方法可如何分类？
- 方法与思路
- 作者以因果工具分析图神经网络的可信度风险，按因果推理与因果表示学习分类，综述典型方法、资源及未来方向。
- 材料陈述的贡献
- 摘要称综述系统整理了因果启发的图神经网络研究，并讨论各类方法缓解可信度风险的方式和后续研究机会。
- 阅读关注点
- 应核对文献纳入范围、分类标准、各方法所需的因果假设，以及可信度改善究竟由理论还是实验比较支持。
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原文摘要
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 many GNN trustworthiness issues can be alleviated by capturing the underlying data causality rather than superficial correlations. In this survey, we comprehensively review recent research efforts on Causality-Inspired GNNs (CIGNNs). Specifically, we first employ causal tools to analyze the primary trustworthiness risks of existing GNNs, underscoring the necessity for GNNs to comprehend the causal mechanisms within graph data. Moreover, we introduce a taxonomy of CIGNNs based on the type of causal learning capability they are equipped with, i.e., causal reasoning and causal representation learning. Besides, we systematically introduce typical methods within each category and discuss 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.