Abstract:
The introduction of various technological solutions for uncovering terrorist networks to enhance public safety and national security is discussed. Under the first generation: manual approach, an analyst must first construct an association matrix by identifying criminal associations from raw data, based on which a link chart for visualization purposes can be drawn. Under the second generation: graphic-based approach, most existing network tools of which include Analysts' Notebook, Netmap, and XANALYS LINK Explorer, the tools automatically produce graphical representations of criminal networks. The third generation: social network analysis (SNA) approach is expected to provide more advanced analytical functionality to assist crime investigation.
Abstract:
The Board on Human-Systems Integration of the US National Research Council (NRC) held a workshop on Unifying Social Frameworks: Sociocultural Data to Accomplish Department of Defense Missions from 16-17 August 2010. Presenters and discussants addressed the variables and complex influenced human behavior, focusing on potential applications to the full spectrum of military operations. Major General Michael T. Flynn of the US Army, delivered the keynote address providing vital information about the cultural situation and needs of the military operating in Afghanistan. Two themes emerged from the workshop, including a theme focusing on data, its collection, its use in models, and the value of analyzing large collections of sociocultural data to identify the groups and individuals that are expected to pose risks in a particular environment.
Abstract:
Because terrorist organizations often operate in network forms where individual terrorists collaborate with each other to carry out attacks, we could gain valuable knowledge about the terrorist organizations by studying structural properties of such terrorist networks. However, previous studies of terrorist network structure have generated little actionable results. This is due to the difficulty in collecting and accessing reliable data and the lack of advanced network analysis methodologies in the field. To address these problems, we introduced the Web structural mining technique into the terrorist network analysis field which, to the best our knowledge, has never been done before. We employed the proposed technique on a Global Salafi Jihad network dataset collected through a large scale empirical study. Results from our analysis not only provide insights for terrorism research community but also support decision making in law-reinforcement, intelligence, and security domains to make our nation safer.
Abstract:
Prediction of gene functions is a major challenge to biologists in the post-genomic era. Interactions between genes and their products compose networks and can be used to infer gene functions. Most previous studies used heuristic approaches based on either local or global information of gene interaction networks to assign unknown gene functions. In this study, we propose a graph kernel-based method that can capture the structure of gene interaction networks to predict gene functions. We conducted an experimental study on a test-bed of P53-related genes. The experimental results demonstrated better performance for our proposed method as compared with baseline methods. © 2007 IEEE.
