Interpretations and Actions towards Trustworthy Graph Neural Networks

图神经网络（GNN）已被广泛采用，在许多实际应用中实现了最先进的性能。b谷歌、亚马逊、Facebook和微软等美国大公司一直在开发基于gnn的大型商业应用程序。这些模型中的大多数都是作为黑盒构建和使用的，不容易向人类解释。缺乏可解释性使得验证和控制gnn的行为变得困难。这带来了严重的安全问题，降低了用户的信任，并阻碍了gnn的大规模部署。为了应对这些问题，各国政府正在起草法律，赋予公民了解自动模型内部工作原理的权利。因此，值得信赖的gnn已成为政府、行业公司和数十亿涉及基于gnn的业务的最终用户的关键需求。开发可信赖的gnn为我们提供了前所未有的能力，以可靠的方式利用大数据，同时，应对重大的技术挑战。本研究计划旨在抓住机遇，及时、切实地应对技术挑战。该研究计划的长期目标是基于对gnn的可靠和一致的解释开发可信赖的商业智能。一般来说，可信赖的商业智能的决策应该对人类是可解释的，对用户是公平的，并且对对抗性攻击是健壮的。我们把长期目标分为四个短期目标。首先，我们将开发有原则的方法，对最初构建和用作黑盒的gnn产生可证明可靠和一致的解释。其次，我们将开发透明的gnn，其原生设计为可解释，同时实现与传统gnn相当的性能。第三，我们将采取基于解释的行动，以增强gnn的公平性和鲁棒性。最后，我们将在实际应用中进行案例研究，以验证和评估我们的研究成果并产生实际影响。我们的研究将在学术界和工业界产生重大影响。在学术界，提出的研究将获得对黑盒gnn的原则性理解，建立有效的方法来构建可信赖的gnn，并探索一大类原则性方法来使用解释来获得更好的模型性能。该研究将推进快速增长的可信大数据分析领域的前沿，并培养具有竞争力的HQP，在学术界产生重大影响。在行业中，我们的一些战略行业合作伙伴已经采用了我们之前的可信机器学习技术；我们希望我们的研究成果更多地用于提升可信赖的商业智能。此外，这项研究将系统地培训不同类型的HQP，使他们具备极具竞争力的技能。向工业界输出先进的技术和高素质的HQP，肯定会产生重大影响。

Graph neural networks (GNN) have been widely adopted to achieve state-of-the-art performance in many real-world applications. Big US companies such as Google, Amazon, Facebook, and Microsoft have been developing large business applications based on GNNs. Most of these models are built and used as black boxes that are not easily interpreted to human. The lack of interpretability makes it difficult to verify and control the behavior of GNNs. This lays severe security problems, reduces the trust from users, and prohibits massive deployments of GNNs. In response to these issues, governments are drafting laws to entitle citizens the right to understand the inner workings of automated models. As a result, trustworthy GNNs have been a critical demand of governments, industry companies, and billions of end users involved in businesses built on GNNs. Developing trustworthy GNNs provide us unprecedented power to leverage big data in a reliable way, and, at the same time, post grand technical challenges. This proposed research program is to embrace the opportunities and address the technical challenges in a timely and practical manner. The long-term objective of this research program is to develop trustworthy business intelligence based on reliable and consistent interpretations on GNNs. In general, the decisions of trustworthy business intelligence should be interpretable to human, fair to users, and robust to adversarial attacks. We divide our long-term objective into four short-term objectives. First, we will develop principled approaches to produce provably reliable and consistent interpretations on GNNs that are originally built and used as black boxes. Second, we will develop transparent GNNs that are natively designed to be interpretable while achieving comparable performance as conventional GNNs. Third, we will take actions based on interpretations to enhance fairness and robustness of GNNs. Last, we will conduct case studies in real applications to verify and evaluate our research outcome and produce practical impact. Our research will have great impacts in both academia and industry. In academia, the proposed research will gain principled understanding on black-box GNNs, establish effective ways to build trustworthy GNNs, and explore a large family of principled approaches to use interpretations for better model performance. The research will advance the frontier in the fast-growing area of trustworthy big data analytics and train competitive HQP to produce substantial impact in academia. In industry, some of our strategic industry partners have already adopted our previous techniques on trustworthy machine learning; and we expect more of our research outcome to be used to boost trustworthy business intelligence. Moreover, this research will systematically train a diverse group of HQP to equip them with highly competitive skill sets. Exporting advanced techniques and talented HQP to industry will certainly produce significant impacts.