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

职业：时态网络分析：模型、算法和应用

• 批准号: 2236789
• 负责人: Ahmet Erdem Sariyuce
• 金额: $ 55.58万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2236789&HistoricalAwards=false
• 关键词: CAREER; Temporal; Network; Analysis; Models

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.

时间网络是一种强大的表示结构，支持理解和表征各种复杂系统。面对面的人际接触、金融交易和计算机通信都可以被视为时间网络，其中交互仅在特定时间点活跃。分析这些网络对于各种应用非常重要，例如在国家安全的背景下维护网络安全环境。另一个例子，在客户端-服务器交互网络的上下文中，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根源，我们能否在执行合法交易的同时检测参与协调加密货币洗钱的账户？该项目设计了一种新的范式，用于有效和高效地分析时间网络，并通过增加公众科学参与，对边缘化社区进行宣传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，以达到高中学生从西班牙裔，缅甸和索马里社区在布法罗地区，告知和教育他们有关的计算机科学和网络科学的基础知识。本项目的成果包括时态网络分析的开源软件框架、入侵检测、反洗钱等关键应用的专业知识等，旨在促进对网络安全、经济、金融、社会网络分析等学科的科学理解。本项目设计并开发基于模体的时态网络分析模型和算法。它将设计一个自下而上的方法，首先在微观上建立原语，然后分析子图和中尺度的周期性，最后扩展在现实世界中遇到的图形技术。该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具有高分辨率和大时间跨度的时间网络上工作。为此，有两个主要的研究重点：（1）时间模体分析的框架;（2）中尺度结构和图形在野外。研究者对所有提出的模型和算法进行理论和实证评估。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应用：（1）双方网络日志中的入侵检测;（2）金融和加密货币交易中的反洗钱。该项目将为图形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。

项目成果

Using Motif Transitions for Temporal Graph Generation

• DOI: 10.1145/3580305.3599540
• 发表时间: 2023-06
• 期刊: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
• 作者: Penghang Liu;Ahmet Erdem Sarıyüce