SITIS Archives - Topic Details
Program:  SBIR
Topic Num:  OSD10-L01 (OSD)
Title:  Temporal and Conceptual Extractions of Complex Social Network Data
Research & Technical Areas:  Information Systems

Acquisition Program:  
  Objective:  Develop a knowledge extraction technique suitable for large datasets of fused multi-source sensor information that can identify and predict adaptations in a terrorist network. Provide a capability for tactical decision makers to understand shifts in terrorist organizations, such as new sources for recruiting members, new concepts for terrorist activities, and temporal elements of terrorist behaviors.
  Description:  A software prototype for discovering and displaying visually temporal and conceptual elements of a social network (that could be extended to a terrorist network). Social network analysis is a valuable methodology for understanding relationships of social groups [1], tracing interactions among individuals [2], and describing data diffusion behavior in delay tolerant networks [3]. The growth of information available from on-line social networking sites and the availability of multi-source sensors has produced a corpus of data that, if sufficient data mining capabilities existed, could aid intelligence analysts in understanding terrorist intentions in near-real time [4]. For such a capability to be realized several developments are required. The first is a focus on communication technologies favored by the social group of interest. Given interest in social groups that may be relatively unknown, such as terrorist groups or emergent social groups [e.g., followers of new trends], a method to deal with and model uncertainty in the context of past behavior may be of potential use [4]. The second is the ability to extract components of social influence in large-scale networks [5]. The knowledge seeker also must identify and track new influences and must be able to determine the spread and strength of followers’ commitment. The third requirement is metrics that define temporal distance in the behavior of the social network. Examples of temporal distance could include frequency of contacts between nodes or groups, inter-contact time, recurrent contacts, time order of contacts along a path, and delay path of information spreading processes [6]. It quickly becomes clear that adding temporal aspects of group behavior in a social network can become difficult to view and, therefore, analyze. Novel visualization techniques will be necessary in order for analysts to extract knowledge, such as those identified in [7]. Perhaps the most difficult but most important requirement for the future of social network analysis is the ability to identify emergent concepts and to link those with observed network behaviors or associations. To the extent that knowledge extraction techniques can identify human conceptualizations, such as the techniques identified in [8], the needle in the haystack may become less elusive. Challenges for this topic include 1) identifying new social network associations that include communication technologies as an identifiable factor, 2) developing methods to extract features associated with social influence in groups, 3) defining and testing metrics for describing temporal aspects of social network behavior, and 4) identifying methods to predict novel concepts that arise from social behavior and attitudes. Several large unclassified datasets will be made available to the contractor to develop these prototype software services, algorithms, and social network metrics. Alternately, the contractor could use open source data sources. The OSD is interested in innovative R&D that involves technical risk. Proposed work should have technical and scientific merit. Creative solutions are encouraged.

  PHASE I: Complete a literature review, feasibility study and research plan that establishes the proof of principle of the approach for analyzing large datasets with a text/biometric software extraction tool capable of producing social network metrics that utilize temporal and conceptual aspects. Identify the critical technology issues that must be overcome to achieve success. Prepare a revised research plan for Phase 2 that addresses critical issues.
  PHASE II: Produce a prototype system that is capable of producing meaningful social network graphs from a corpus of uncertain data that can provide meaningful prediction of human behavior in relation to temporal and conceptual aspects. The prototype should lead to a demonstration of the capability. Test the prototype in an environment to demonstrate feasibility. Demonstrate a capability to construct social network graphs using novel algorithms that support conceputal knowledge discovery in dynamic time periods.

  PHASE III: Produce a system capable of deployment in an operational setting of interest. Test the system in an operational setting in a stand-alone mode or as a component of larger system. The work should focus on capability required to achieve transition to program of record of one or more of the military Services. The system should provide metrics for performance assessment.

  References:  1. Waskiewicz, T. & LaMonica, P. (2008). Developing an intelligence analysis process through social network analysis. In M. Blowers and Alex F. Sisti, Eds., Evolutionary and Bio-Inspired Computation: Theory and Applications II, Proceedings of SPIE Vol. 6964, 69640B. 2. Kahn, M. U. & Khan, S. A. (2009). Social networks identification and analysis using call detail records. ACM: ICIS, November 24-26, Seoul, Korea. 3. Zhang, Y. & Zhao, J. (2009). Social network analysis on data diffusion in delay tolerant networks. ACM: Proceedings of MobiHoc 09, May 18-21, New Orleans, LA. 4. Drozdova, K. & Samoilov, M. (2009). Predictive analysis of concealed social network activities based on communication technology choices: Early-warning detection of attack signals from terrorist organizations. Computational Math Organizational Theory, DOI 10.1007/s10588-009-9058-2. 5. Tang, J., Sun, J., Wang, C. & Yang, Z. (2009). Social influence analysis in large-scale networks. ACM Proceedings: KDD 09, June 28- July 1, Paris, France. 6. Tang, J., Musolesi, M., Mascolo, C. & Latora, V. (2009). Temporal distance metrics for social network analysis. ACM: WOSN 09 Proceedings, August 17, 2009, Barcelona, Spain. 7. Kang, H., Getoor, L. & Sing, L. (2007). Visual analysis of dynamic group membership in temporal social networks. SIGKDD Explorationa Newsletter, December 2007, Vol 9, Iss 2. 8. Yager, R. R. (2008). Intelligent social network analysis using granular computing. International Journal of Intelligent Systems, Vol 23, 1197-1220.

Keywords:  social networks, temporal aspects, conceptual extraction, visualization techniques, social graphs, algorithms

Questions and Answers:

No questions posed on this topic at this time

Record: of