Collaborative Research: Framework Implementation: CSSI: CANDY: Cyberinfrastructure for Accelerating Innovation in Network Dynamics

动态网络的高效分析在各种多学科的现实生活应用中非常重要，例如数据挖掘和分析、社会和生物网络、流行病学、网络物理基础设施、交通网络、地面采矿和网络安全。尽管有许多软件用于分析静态网络，但缺乏全面的网络基础设施来支持大规模、复杂、动态网络中的创新研究挑战。这项由多所大学提出的提案旨在解决这一问题，它开发了一个名为CIDEY(网络基础设施加速网络动力学创新)的新平台，该平台基于针对动态网络的高效、可扩展的并行算法设计和具有性能优化的高性能软件开发。对于更广泛的影响和外联活动，调查人员将(1)与多学科研究小组合作，评估开发的平台、算法和软件工具的有效性；(2)主办研讨会、网络研讨会和教程，以教育研究社区有关网络基础设施；(3)通过专门的网站、主旨演讲和特邀演讲、演示以及同行评议期刊和会议上的高质量出版物传播项目成果；以及(4)通过吸引妇女和代表性不足的少数族裔学生，包括密苏里州的高中生和农村社区，德克萨斯州的西班牙裔和非裔美国人社区，以及俄勒冈州的第一原住民(印第安人)社区，培训下一代数据科学家开发糖果平台。该项目将开发第一个并行、可扩展、可扩展和用户友好的软件平台，用于更新动态网络的重要属性。它还将提供必要的功能和工具，以修改现有算法或创建新的算法，以迎合具有不同专门知识水平的基本、中级和高级用户。Candy网络基础设施平台将在不同的架构上实施，如分布式内存、共享内存和提供用户友好界面的图形处理器单元。重要的研究和开发创新包括：(1)新的网络分析算法的分层分类，允许基于多参数的并行算法的分层规范；(2)用于创建用于动态网络分析的新的可扩展算法的模板；(3)在变化点将节点和边的流集划分为网络快照的算法；以及(4)用于分析大规模动态网络的基于不变量的可量化性能度量。作为案例研究，开发的软件将在两个不同的领域进行评估--动态树上基因组数据的快速处理，以及复杂采矿工程应用的成本效益操作。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Efficient analysis of dynamic networks is highly important in diverse multidisciplinary real-life applications, such as data mining and analytics, social and biological networks, epidemiology, cyber-physical infrastructures, transportation networks, surface mining, and cybersecurity. Although numerous software exists for analyzing static networks, a comprehensive cyberinfrastructure that supports innovative research challenges in large-scale, complex, dynamic networks is lacking. This multi-university proposal addresses this gap by developing a novel platform, called CANDY (Cyberinfrastructure for Accelerating Innovation in Network Dynamics), based on efficient, scalable parallel algorithm design for dynamic networks and high-performance software development with performance optimization. For broader impact and outreach activities, the investigators will (1) collaborate with multidisciplinary research groups to evaluate the effectiveness of the developed platform, algorithms and software tools; (2) host workshops, webinars, and tutorials to educate research community about the cyberinfrastructure; (3) disseminate project outcomes via a dedicated website, keynote and invited talks, demos, and high-quality publications in peer-reviewed journals and conferences; and (4) train next generation data scientists in the development of CANDY platform, by engaging women and underrepresented minority students, including high school students and rural communities in Missouri, Hispanic and African-American communities in Texas, and First Nation (Native American) community in Oregon.This project will develop the first parallel, scalable, extendable, and user-friendly software platform for updating important properties of dynamic networks. It will also provide the requisite functionalities and tools to modify existing algorithms or create new ones, catering to basic, intermediate and advanced users with different levels of expertise. The CANDY cyberinfrastructure platform will be implemented on different architectures, such as distributed memory, shared memory, and graphics processor units providing user-friendly interfaces. Significant research and development innovations include: (1) a novel hierarchical taxonomy of network analysis algorithms that allows for layered specification of parallel algorithms based on multiple parameters; (2) templates for creating new scalable algorithms for dynamic network analysis; (3) algorithms to partition the streaming set of nodes and edges into network snapshots at changing points; and (4) invariant-based quantifiable performance metrics for analyzing large-scale dynamic networks. As a case study, the developed software will be evaluated on two disparate domains -- fast processing of genomic data on dynamic trees, and cost-effective operation of complex mining engineering applications.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.