High Fidelity Synthesis of Dynamic Social Networks using Measurement-based Calibration

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-13-C-0055
Agency Tracking Number: D2-1174
Amount: $1,749,998.00
Phase: Phase II
Program: SBIR
Awards Year: 2012
Solicitation Year: 2011
Solicitation Topic Code: SB112-004
Solicitation Number: 2011.2
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive Suite 400, Rockville, MD, 20855
DUNS: 000000000
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Yalin Sagduyu
 Program Manager
 (301) 294-5267
 ysagduyu@i-a-i.com
Business Contact
 Mr. Mark James
Title: Director, Contracts and P
Phone: (301) 294-5267
Email: mjames@i-a-i.com
Research Institution
 Stub
Abstract
We propose to develop a systematic approach of synthetically generating diverse types of large-scale, dynamic and high fidelity social network data via measurement calibration. Social network research needs access to realistic social media datasets for testing social network theory and developing network algorithms. However, the available social network data are either small, static, or cannot be readily collected or shared due to anonymization, privacy, and complexity issues. We will design and implement scalable algorithms, methods and software tools to generate realistic social network data with respect to multi-dimensional network analytical functions and validate it with a comprehensive set of statistical, temporal, and topological metrics, and application-level benchmarks. Our key innovative solution is based on capturing dynamic (visible or latent/hidden) interactions, patterns/anomalies, information flows/cascades along with the coupled dynamics of network structure and data content (topics, sentiments, and memes) to synthesize social media. Our approach generates fully anonymous social network data that can be distributed for public use by researchers. By leveraging advanced tools from graph-theoretic, statistical, topological and time-series analysis, we will provide an integrated architecture and technology base to support diversity of emerging social media types and deploy strategic social network applications for high fidelity social network synthesis.

* information listed above is at the time of submission.

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