Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications

图神经网络（gnn）将深度学习的成功转化为图结构数据，具有从推荐系统和欺诈检测到医学到金融的众多应用。在这样的应用中，相似实体彼此连接的程度（称为同质性）是未知的，并且由于有限的标记数据而无法通过经验计算。虽然同质性是常见的，但它不是普遍的；在一些重要的现实世界中，“异性相吸”会导致异性恋（低同质性）。通过超越对图同质性的依赖并引入新的GNN模型，该项目将使GNN在更广泛的领域中有效地工作。它还将有助于纠正为同态图定制的gnn的一些负面后果，包括应用于异态数据时的有偏见、不公平或错误的预测。关注鲁棒性、公平性和可解释性将有助于在使用GNN模型的领域支持负责任的算法决策。除了研究之外，该项目还将支持密歇根大学、新泽西理工学院和密歇根州立大学的本科生和研究生的培训，通过将该研究整合到高级课程、顶点项目和其他直接为该研究项目做出贡献的机会中。gnn无法将其在同质图或分类图上的强大性能推广到许多异亲图上，这引起了人们的极大关注，并导致了“良好异亲性”的存在的实证证明，在这种情况下，gnn可以表现良好。然而，对于gnn容易或难以处理的异质性类型的理解仍然有限，特别是超出了有限的，典型的研究设置（即小同构图上的节点分类）。该项目将推进不同类型的异质性和gnn之间相互作用的理论基础，考虑部署所必需的精度以外的特性。具体来说，它将有助于：(a)新理论：它将正式描述与gnn的异亲性相关的挑战，以提供对“好”和“坏”异亲性的更深入理解，并增强我们对“好”类型的异亲性的理解，一些架构可以有效地建模，但迄今为止一直被极大地忽视。(b)新模型：基于新理论，它将引入新的GNN设计和架构，这些设计和架构不仅在不同层次和类型的异质性中具有强大的性能，而且还具有鲁棒性，公平性和透明度，这对算法决策至关重要。(c)新的应用：该项目还将超越文献中调查的传统任务和不同类型的网络，并将包括与学术界和工业界的合作者一起探索具有高度影响的应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Graph neural networks (GNNs), which translate the success of deep learning to graph-structured data, have numerous applications spanning from recommendation systems and fraud detection to medicine to finance. In such applications, the extent to which similar entities connect with each other---known as homophily---is unknown and cannot be computed empirically due to limited labeled data. Though homophily is common, it is not universal; there are important real-world settings where "opposites attract", leading to heterophily (low homophily). By moving beyond a reliance on graph homophily and introducing new GNN models, this project will generalize GNNs to work effectively in a wider range of domains. It will also help rectify some negative consequences of GNNs that are tailored to homophilous graphs, including biased, unfair, or erroneous predictions when applied to heterophilous data. Focusing on robustness, fairness, and explainability will help support accountable algorithmic decision-making in the domains where GNN models are employed. In addition to research, this project will support the training of a diverse cohort of undergraduate and graduate students at the University of Michigan, the New Jersey Institute of Technology, and Michigan State University via integration of this research in advanced courses, capstone projects, and other opportunities to directly contribute to this research program.The inability of GNNs to generalize their strong performance on homophilous or assortative graphs to many heterophilous graphs has attracted significant attention, and has led to empirical demonstration of the existence of "good heterophily", where GNNs can perform well. However, there is still limited understanding about the types of heterophily that are easy or difficult to handle with GNNs, especially beyond the limited, typically-studied settings (i.e., node classification on small homogeneous graphs). This project will advance the theoretical underpinnings of the interplay between different types of heterophily and GNNs, considering properties beyond just accuracy, which are necessary for deployment. Specifically, it will contribute: (a) New Theory: It will formally characterize the heterophily-related challenges of GNNs to provide a deeper understanding into "good" and "bad" heterophily, and enhance our understanding of "good" types of heterophily, which some architectures can model effectively, but have been vastly ignored until now. (b) New Models: Based on the new theory, it will introduce new GNN designs and architectures that not only have strong performance across different levels and types of heterophily, but are also robust, fair, and transparent, which are crucial for algorithmic decision-making. (c) New Applications: The project will also go beyond the traditional tasks and heterophilous network types investigated in the literature, and will include exploration of high-impact applications along with collaborators in academia and industry.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.