Techniques to Interdict and Monitor Cohesiveness in Dark Networks

拦截和监控暗网络凝聚力的技术

• 批准号: 1634550
• 负责人: Illya Hicks
• 金额: $ 30万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634550&HistoricalAwards=false
• 关键词: Techniques; Interdict; Monitor; Cohesiveness; Dark

Dark social networks consist of illegal and covert networks of people. Given the nature of being "dark", it is vital to disrupt the network through capture (interdiction) or intelligence gathering (monitoring). In contrast, information on these networks is often incomplete, inaccurate or not available. Gathering information on dark networks dates back to World War II and has become more prevalent today given the current climate of "The War on Terror"; the increased access to information; and the usage of social network analysis to analyze these networks. This award supports fundamental research using innovative mathematical techniques to interdict or monitor cohesiveness within dark social networks. This award also supports a concerted effort to engage underrepresented groups within the research and positively impact engineering education.In particular, the research team will utilize new techniques within branch decomposition methods and integer programming to investigate the clique-interdiction problem, the minimum clique-transversal problem and the variations of the problems where "clique" is replaced with k-plex, k-clique or k-club. This research will advance the knowledge base of combinatorial optimization and integer programming while also contributing to the increased scalability and efficiency for solving computationally hard problems related to homeland security, criminal justice, and marketing.

黑暗社交网络由非法和隐蔽的人际网络组成。鉴于“黑暗”的性质，通过捕获（拦截）或情报收集（监测）来破坏网络至关重要。相反，这些网络上的信息往往不完整、不准确或无法获得。在黑暗网络上收集信息可以追溯到第二次世界大战，并且由于当前的“反恐战争”气候，今天变得更加普遍;信息获取的增加;以及使用社交网络分析来分析这些网络。 该奖项支持使用创新的数学技术来阻断或监控黑暗社交网络内的凝聚力的基础研究。 该奖项还支持在研究中参与代表性不足的群体并对工程教育产生积极影响的共同努力。特别是，研究团队将利用分支分解方法和整数规划中的新技术来研究集团阻断问题，最小集团横向问题以及将“集团”替换为k-plex，k-clique或k-club的问题的变体。 这项研究将推进组合优化和整数规划的知识基础，同时也有助于提高解决与国土安全，刑事司法和营销相关的计算困难问题的可扩展性和效率。