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，可支持理解和表征各种复杂系统。面对面的人类联系，金融交易和计算机通信都可以看作是时间网络，在某些时间点相互作用仅在某些时间点处于活动状态。分析此类网络对于各种应用程序很重要，例如在国家安全的背景下维持网络安全环境。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有一组服务器以异常协调的方式与客户端进行交互。在洗钱的背景下，犯罪活动的命脉和美国经济竞争力的损害来源，我们是否可以在同时同时进行同时执行LICIT交易的同时检测到协调的加密货币洗钱的帐户？该项目设计了一个新的范式，用于有效，有效地分析时间网络，并通过增加公共科学参与，向边缘化社区进行宣传以及课程发展，从而培训来自不同背景的计算机科学家的下一代。研究人员特别组织研讨会，以吸引布法罗地区西班牙裔，缅甸和索马里人社区的高中生，以告知和教育他们有关计算机科学和网络科学的基础知识。诸如用于时间网络分析的开源软件框架以及在诸如入侵检测和反货币洗衣之类的关键应用方面的知识之类的输出旨在提高和促进网络安全，经济性，财务，财务和社交网络分析等各种学科的科学理解。这些项目设计和基于基于基于基于的模型的模型和Algoriths和Algoriths Analgorith和Process和Process Analgeme和Process Analgeze和Processals和Processals Analge和Processals Analgese和Processals Analgeze和Processals Analgeze和Processals Analgeze和Processals。它将在自下而上的方法中设计通用的形式，然后在微观尺度上首次构建原始图，然后分析中尺度的子图和周期性，最后扩展在现实世界应用中遇到的图形技术。该项目通过新的模型和算法扩大了知识，这些模型和算法可以在具有良好分辨率和大型时间内的时间网络上使用。为此，有两个主要的研究作用：（1）时间基序分析的框架； （2）野外的中尺度结构和图形。研究者对所有提出的模型和算法进行理论和经验评估。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）在两部分网络对数中的入侵检测； （2）金融和加密货币交易中的反洗钱。该项目将为图挖掘和网络科学的领域做出贡献。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。

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