. "Dynamic Network Analysis in Counterterrorism Research--Kathleen Carley, Carnegie Mellon University." Proceedings of a Workshop on Statistics on Networks. Washington, DC: The National Academies Press, 2007.
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Proceedings of a Workshop on Statistics on Networks
important connections and so on, or what is the health of the organization, how has it been changing over time? Can we infer where there is missing data, which helps to focus intelligence gathering ideas? How different are groups? And so on. There is a whole slew of questions like these that need to be addressed, some very theoretical while others are of near-term, pragmatic interest, such as “What is the immediate impact of a particular course of action?” These are the kinds of questions that need to be addressed using network inspired tools. The tools that I will talk about today are the first tiny steps on the long road to helping people address these kinds of questions and meeting this very real and practical need.
From a technical perspective what we want to do is be able to provide a system of evaluation for looking at change in multi-mode, multi-plex, dynamic networks. Maybe they are terror networks today, maybe they are drug networks tomorrow, but there is a whole set of these kinds of networks, and we want to analyze them under conditions of uncertainty. Finally, we want to place predictions in a risk context: that is, given sparse and erroneous data we want to estimate the probabilities of events and the likelihood of other inferences and the confidence interval around these estimates.
From a user’s point of view it is imperative that we provide the tools and the ability to think about analysis and policy issues from an end-to-end perspective. We all know that network tools are extremely data greedy, so we need to embed them in a larger context of bringing in the data automatically, analyzing it automatically, and using different kinds of prediction capabilities to make forecasts and so basically free up human time to do real live analysis and interpretation. At CMU we have developed a few tools as shown in Figure 1, but these are just examples of a lot of the tools that are out there. For every tool I will mention there are dozens more that more or less meet a similar purpose. Overall, our tool chain serves to bring in a set of raw text, like newspaper reports, and, using various entity extraction and language technology, identifies various networks. These are networks of people to people, people to ideas, people to events, et cetera. We then take those networks and analyze them. We are using a tool called ORA1 for doing that, which lets us do things like identify key actors, groups, and so on. Once we have done that, the tools give us some courses of action that we might want to analyze. We take those, put them into a simulation framework, and evolve the system forward at time. All of this sits on top of data bases, etc. Basically, the set of tools help you build a network so you may find points of influence, and then help you assess strategic intervention. The important thing from a technology standpoint is that all of these things have to be built by lots of people, they have to be
ORA is a statistical toolkit for meta-matrices that identifies vulnerabilities, key actors (including emergent leaders), and network characteristics of groups, teams, and organizations.