CAREER: Temporal Network Analysis: Models, Algorithms, and Applications

时态网络是一种强大的表示结构，支持对各种复杂系统的理解和刻画。面对面的人类接触、金融交易和计算机通信都可以被视为仅在特定时间点活跃的交互的时间网络。分析这类网络对于各种应用都很重要，例如在国家安全的背景下维护网络安全环境。在客户端-服务器交互网络的上下文中，另一个示例是确定是否存在一组以异常协调的方式与客户端交互的服务器。在洗钱的背景下，洗钱是犯罪活动的命脉，也是损害美国经济竞争力的来源，我们能否在进行合法交易的同时，检测到参与协同加密货币洗钱的账户？该项目为有效和高效地分析时态网络设计了一个新的范例，并通过增加公众科学参与、向边缘化社区进行外联和课程开发来培训来自不同背景的下一代计算机科学家。特别是，调查员组织研讨会，接触来自布法罗地区西班牙裔、缅甸和索马里社区的高中生，向他们通报和教育计算机科学和网络科学的基础知识。这些成果，如用于时态网络分析的开源软件框架和入侵检测和反洗钱等关键应用程序的技术诀窍，旨在促进和促进网络安全、经济、金融和社会网络分析等不同学科的科学理解。该项目设计和开发基于Motif的模型和算法来分析和处理时态网络。它将以自下而上的方法设计通用的形式化方法，首先在微观尺度上建立基元，然后在中尺度上分析子图和周期性，最后扩展在现实世界应用中遇到的图的技术。这个项目通过新的模型和算法扩展了知识，这些模型和算法可以在高分辨率和大时间跨度的时间网络上工作。为此，有两个主要的研究方向：(1)时间主题分析的框架；(2)野外的中尺度结构和图形。研究人员对所有提出的模型和算法进行了理论和经验评估。特别是，该项目考虑了与行业和政府研究实验室合作的两个现实世界应用；(1)双向网络日志中的入侵检测；以及(2)金融和加密货币交易中的反洗钱。该项目将为图挖掘和网络科学领域做出贡献。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。

Temporal networks are a powerful representation structure that support understanding and characterizing various complex systems. Face-to-face human contacts, financial transactions, and computer communications can all be viewed as temporal networks where interactions are active only at certain points in time. Analyzing such networks is important for various applications such as maintaining cyber-secure environments in the context of national security. Another example, in the context of a client-server network of interactions, is determining whether there is a set of servers that interact with clients in an unusually coordinated way. In the context of money laundering, the lifeblood of criminal activities and a source of damages to economic competitiveness in the U.S., can we detect the accounts involved in coordinated cryptocurrency laundering while also performing licit transactions? This project devises a new paradigm for analyzing temporal networks effectively and efficiently and trains next-generation of computer scientists from diverse backgrounds by increasing public scientific engagement, performing outreach to marginalized communities, and course development. In particular, the investigator organizes workshops to reach high-school students from Hispanic, Burmese, and Somalis communities in the Buffalo area to inform and educate them about the basics of computer science and network science. Outputs, such as an open-source software framework for temporal network analysis and know-how on critical applications such as intrusion detection and anti-money laundering, are designed to advance and contribute to scientific understanding in various disciplines such as cybersecurity, economics, finance, and social network analysis.This project designs and develops motif-based models and algorithms to analyze and process temporal networks. It will devise generic formalizations in a bottom-up approach by first building primitives in the microscale, then analyzing the subgraphs and periodicity in the mesoscale, and lastly extending the techniques for graphs encountered in real-world applications. This project broadens the knowledge with new models and algorithms that can work on temporal networks with fine resolution and a large timespan. To this end, there are two main research thrusts: (1) a framework for temporal motif analysis; and (2) mesoscale structures and graphs in the wild. The investigator performs theoretical and empirical evaluations for all the proposed models and algorithms. In particular, this project considers two real-world applications in collaboration with industry and government research labs; (1) intrusion detection in bipartite cyber logs; and (2) anti-money laundering in financial and cryptocurrency transactions. This project will make contributions to the fields of graph mining and network science.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.