Multi-Scale, Multi-Resolution Network Information Flow Monitoring and Understanding

Award Information
Department of Defense
Air Force
Award Year:
Phase II
Agency Tracking Number:
Solicitation Year:
Solicitation Topic Code:
Solicitation Number:
Small Business Information
Intelligent Automation, Inc.
15400 Calhoun Drive, Suite 400, Rockville, MD, -
Hubzone Owned:
Socially and Economically Disadvantaged:
Woman Owned:
Principal Investigator:
Yalin Sagduyu
Research Scientist
(301) 294-5267
Business Contact:
Mark James
Director, Contracts and Proposals
(301) 294-5221
Research Institution:
Princeton University
Princeton University
B328, Engineering Quad
Olden Street
Princeton, NJ, 08544-
(659) 258-5071
Nonprofit college or university
ABSTRACT: We propose a unifying framework for multi-scale multi-resolution inference, analysis, and design of complex socio-technological networks. Our key innovation is to combine graph-analytical methods with functional analysis through information-geometric optimization across time, space, and frequency. Isolated approaches separating social network analysis and communication network design cannot capture and exploit the strong interactions forming socio-technological networks. Our focus is modeling, monitoring, and analyzing the interdependence of social and communication networks, network dynamics, and the impact of social influence spread in heterogeneous complex networks. We reflect social functions regarding search, queering, and information dissemination as generalized forms of information flows across socio-technological networks, and integrate them with dynamic graph analysis. This allows monitoring and inference of information flow structures embedded in geometric representations. Then, social network properties can be reverse-engineered from communication network statistics along with the underlying geometric structures. These network inference results offer new guidelines for systematic design of communication network protocols with social network functionalities. This effort supports anomaly detection, network recovery and social influence control in robust operation while improving the general performance of communication networks. Our analytical framework is complemented with socio-technological experimentations and offers powerful tools to improve robustness, adaptability, and survivability in information networks. BENEFIT: Complex network inference, analysis, and design framework leverages social and communication network interactions and has great potential to facilitate holistic understanding of network information flows, robust networking, anomaly detection, network recovery, and social influence spread in military and commercial networked organizations. If successful, our work can provide multi-resolution multi-scale insights on social network associations, temporal dynamics, and network statistics, based on monitoring, inferring and understanding structural properties of network information flows. This synergistic effort will greatly benefit the Air Force Office of Scientific Research as well as other government agencies via providing both fundamental understanding and practical implementation. One direct product of this research will be a socio-technological analysis tool, which will be beneficial to integrated defense and surveillance, anomaly detection, network recovery, integrated communications systems, resource discovery, knowledge management and exploitation in military applications. There is a broad range of commercial applications with special focus on detecting changes in the organizational behavior (e.g., to capitalize on innovation in a corporate setting), detecting and preventing failures and attacks.

* information listed above is at the time of submission.

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