当图神经网络遇上因果性：机遇、方法与展望
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
AI总结：
本文综述因果启发的图神经网络（CIGNNs），用因果工具分析 GNN 可信性风险，并按因果推理与因果表示学习梳理方法、资源和未来方向。
摘要：
图神经网络（GNNs）已成为强大的表示学习工具，用于捕捉各类图结构数据中的复杂依赖关系。尽管 GNNs 在广泛的图挖掘任务中取得了成功，但其可信性引发了严重担忧，包括易受分布偏移影响、对某些群体存在偏见，以及缺乏可解释性。近年来，将因果学习技术整合到 GNNs 中催生了大量开创性研究，因为许多 GNN 可信性问题可以通过捕捉底层数据因果性而非表面相关性得到缓解。在本综述中，我们全面回顾了关于因果启发的 GNNs（Causality-Inspired GNNs，CIGNNs）的近期研究工作。具体而言，我们首先运用因果工具分析现有 GNNs 的主要可信性风险，强调 GNNs 理解图数据中因果机制的必要性。此外，我们根据 CIGNNs 所具备的因果学习能力类型，即因果推理和因果表示学习，提出了一种分类体系。我们还系统介绍了每个类别中的典型方法，并讨论它们如何缓解可信性风险。最后，我们总结了有用资源，并讨论若干未来方向，希望为这一新兴领域的新研究机遇提供启发。代表性论文以及开源数据和代码可在 https://github.com/usail-hkust/Causality-Inspired-GNNs 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
