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

合作研究：III：媒介：异质数据的图神经网络：推进理论、模型和应用

• 批准号: 2406648
• 负责人: Yao Ma
• 金额: $ 40万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-15 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2406648&HistoricalAwards=false
• 关键词: Collaborative; Research; III; Medium; Graph

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.

图形神经网络（GNN）将深度学习的成功转化为图形结构化数据，具有从推荐系统和欺诈检测到医学到融资的许多应用程序。在这样的应用中，相似实体相互连接的程度（称为同质性）是未知的，由于标记的数据有限，因此无法通过经验计算。虽然同质性是普遍的，但它并不普遍。有重要的现实环境，“对方吸引”，导致异性恋（低同质性）。通过超越对图形同质图的依赖并引入新的GNN模型，该项目将推广GNN在更广泛的域中有效地工作。它还将有助于纠正针对同质图量身定制的GNN的一些负面后果，包括应用于异质数据时有偏见，不公平或错误的预测。专注于鲁棒性，公平性和解释性将有助于支持使用GNN模型的领域中负责任的算法决策。除研究外，该项目还将支持密歇根大学，新泽西大学技术研究所和密歇根州立大学的多样化的本科生和研究生培训，通过在高级课程，Capstone项目和其他机会中融合这项研究，以及其他机会直接为这项研究计划做出贡献。注意，并导致了对“良好异性恋”的存在的经验证明，GNN可以表现良好。但是，对GNN的异质性类型仍然有限，尤其是在有限的，通常研究的设置（即，在小均匀图上的节点分类）之外，很容易或难以处理。该项目将推进不同类型的异质和GNN之间相互作用的理论基础，考虑到不仅准确的属性，这是部署所必需的。具体而言，它将做出贡献：（a）新理论：它将正式表征GNN与异性相关的挑战，以对“好”和“坏”杂质提供更深入的了解，并增强我们对“良好”类型的异性恋类型的理解，这些类型的异性含量可以有效地模型，但是直到现在直到现在。 （b）新模型：基于新理论，它将引入新的GNN设计和体系结构，这些设计和体系结构不仅在不同级别和异性级别上具有强大的性能，而且还具有稳健，公平和透明，这对于算法决策至关重要。 （c）新应用：该项目还将超越文献研究中调查的传统任务和异性网络类型，并将包括探索高影响力应用程序以及学术界和行业的合作者。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子优点和广泛的效果来评估NSF的法定任务。