Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

Michael R. Margitus; William A. Tagliaferri Jr.; Moises Sudit; Peter M. LaMonica

doi:10.1117/12.920598

3 May 2012 Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis

Michael R. Margitus, William A. Tagliaferri Jr., Moises Sudit, Peter M. LaMonica

Author Affiliations +

Proceedings Volume 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI; 84020E (2012) https://doi.org/10.1117/12.920598
Event: SPIE Defense, Security, and Sensing, 2012, Baltimore, Maryland, United States

Abstract

Understanding the structure and dynamics of networks are of vital importance to winning the global war on terror. To fully comprehend the network environment, analysts must be able to investigate interconnected relationships of many diverse network types simultaneously as they evolve both spatially and temporally. To remove the burden from the analyst of making mental correlations of observations and conclusions from multiple domains, we introduce the Dynamic Graph Analytic Framework (DYGRAF). DYGRAF provides the infrastructure which facilitates a layered multi-modal network analysis (LMMNA) approach that enables analysts to assemble previously disconnected, yet related, networks in a common battle space picture. In doing so, DYGRAF provides the analyst with timely situation awareness, understanding and anticipation of threats, and support for effective decision-making in diverse environments.

Citation Download Citation

Michael R. Margitus, William A. Tagliaferri Jr., Moises Sudit, and Peter M. LaMonica "Dynamic Graph Analytic Framework (DYGRAF): greater situation awareness through layered multi-modal network analysis", Proc. SPIE 8402, Evolutionary and Bio-Inspired Computation: Theory and Applications VI, 84020E (3 May 2012); https://doi.org/10.1117/12.920598

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