III: Small: Learning to Hash Information Networks

III：小：学习散列信息网络

• 批准号: 2007175
• 负责人: Yong Ge
• 金额: $ 49.96万
• 依托单位: University of Arizona
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2007175&HistoricalAwards=false
• 关键词: III; Small; Learning; Hash; Information

Many of today's real-world systems form information networks, where nodes represent multi-type entities and links reflect relation between entities. Examples include social networks, user-product networks, and knowledge graphs, that exist in social networking sites, e-commerce systems, and digital encyclopedia systems, respectively. One major goal of data science is to obtain effective representations of information networks to support various analytical tasks such as similarity search, link prediction, and node ranking that enable real-world applications such as search and recommendation. Prior research has developed machine learning and optimization solutions for inferring network data representations. However, these approaches still suffer from computation and storage challenges, especially while responding to real-time requests and computing on small devices. This project will develop novel methods for learning network data representations that could save much computational cost and storage space. Ultimately, the project will make the applications driven by network data run more effectively and efficiently. This project will develop novel machine learning methods for hashing both homogeneous and heterogenous information networks. In addition to structure information, the developed approach will also consider attribute information that may be available for many information networks. As the formalized learning objectives are NP-hard problems with binary decision variables, this project will develop new optimization methods for solving the learning problems by exploring two different directions for solving the learning problems. The first one is to creatively transform the formalized learning problems to well-studied Max-Cut problems and then leverage existing approaches to solve the transformed problems. The other one is to study continuous optimization reformulations for the formalized learning problems and then develop convex approximations to solve the reformulated continuous optimization problems. The learned binary representations of information networks will be applied to several important applications tasks such as similar node search, node classification, link prediction, and recommendations. Multiple information network datasets will be used to evaluate the performance of developed methods based on different quantitative evaluation metrics. The developed information network hashing solutions in this project will significantly advance the research fields of representation learning of information networks, data mining and machine learning for network data analysis, and optimization for machine learning problems.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.

当今的许多现实世界的系统形成信息网络，其中节点代表多类型的实体和链接反映实体之间的关系。示例包括分别存在于社交网站、电子商务系统和数字百科全书系统中的社交网络、用户产品网络和知识图。数据科学的一个主要目标是获得信息网络的有效表示，以支持各种分析任务，如相似性搜索，链接预测和节点排名，从而实现搜索和推荐等现实应用。先前的研究已经开发了用于推断网络数据表示的机器学习和优化解决方案。然而，这些方法仍然受到计算和存储挑战的影响，特别是在响应实时请求和在小型设备上计算时。这个项目将开发新的方法来学习网络数据表示，可以节省大量的计算成本和存储空间。最终，该项目将使网络数据驱动的应用程序更有效地运行。该项目将开发新的机器学习方法，用于散列同质和异质信息网络。除了结构信息，开发的方法也将考虑属性信息，可能是许多信息网络。由于形式化的学习目标是具有二元决策变量的NP-难问题，因此本项目将通过探索解决学习问题的两个不同方向来开发新的优化方法。第一种方法是创造性地将形式化学习问题转化为已有研究的最大割问题，然后利用已有的方法解决转化后的问题。另一种是研究形式化学习问题的连续优化重构，然后发展凸逼近来求解重构的连续优化问题。学习的信息网络的二进制表示将被应用于几个重要的应用任务，如相似节点搜索，节点分类，链接预测和推荐。多个信息网络数据集将被用来评估基于不同的定量评估指标的开发方法的性能。该项目中开发的信息网络哈希解决方案将极大地推进信息网络的表示学习、网络数据分析的数据挖掘和机器学习以及机器学习问题的优化等研究领域。该奖项反映了NSF的法定使命，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。