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

  • M. R. Margitus
  • , W. A. Tagliaferri
  • , M. Sudit
  • , P. M. Lamonica
  • CUBRC
  • Air Force Research Laboratory

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationEvolutionary and Bio-Inspired Computation
Subtitle of host publicationTheory and Applications VI
DOIs
StatePublished - 2012
EventEvolutionary and Bio-Inspired Computation: Theory and Applications VI - Baltimore, MD, United States
Duration: Apr 25 2012Apr 26 2012

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume8402
ISSN (Print)0277-786X

Conference

ConferenceEvolutionary and Bio-Inspired Computation: Theory and Applications VI
Country/TerritoryUnited States
CityBaltimore, MD
Period04/25/1204/26/12

Keywords

  • Dynamic Networks
  • Information Fusion
  • Layered Multi-modal Network Analysis
  • Situation Awareness
  • Social Network Analysis

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