Techniques to Interdict and Monitor Cohesiveness in Dark Networks

黑暗社交网络由非法和隐蔽的人际网络组成。考虑到“黑暗”的本质，通过抓捕（拦截）或情报收集（监控）来破坏网络是至关重要的。相比之下，这些网络上的信息往往不完整、不准确或无法获得。在暗网络上收集信息的历史可以追溯到第二次世界大战，在当前“反恐战争”的大环境下，这种信息收集在今天变得更加普遍；获取信息的机会增加；并使用社会网络分析来分析这些网络。该奖项支持使用创新数学技术来拦截或监控黑暗社会网络内的凝聚力的基础研究。该奖项还支持协调一致的努力，使研究中代表性不足的群体参与进来，并对工程教育产生积极影响。特别是，研究小组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的问题的变化。本研究将提升组合优化和整数规划的知识基础，同时也有助于提高可扩展性和效率，以解决与国土安全、刑事司法和市场营销相关的计算难题。

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.