Multi-scale Dynamic Network Graph (MUSING) for Network-based Information Exploitation

Award Information
Agency:
Department of Defense
Amount:
$70,000.00
Program:
SBIR
Contract:
N00014-08-M-0208
Solitcitation Year:
2008
Solicitation Number:
2008.1
Branch:
Navy
Award Year:
2008
Phase:
Phase I
Agency Tracking Number:
N081-081-1159
Solicitation Topic Code:
N08-081
Small Business Information
SCIENTIFIC SYSTEMS CO., INC.
500 West Cummings Park - Ste 3000, Woburn, MA, 01801
Hubzone Owned:
N
Woman Owned:
N
Socially and Economically Disadvantaged:
Y
Duns:
859244204
Principal Investigator
 Ssu-Hsin Yu
 Grp Lead: Signal Image Processing
 (781) 933-5355
 syu@ssci.com
Business Contact
 Jay Miselis
Title: Corporate Controller
Phone: (781) 933-5355
Email: jmiselis@ssci.com
Research Institution
N/A
Abstract
As previously dispersed information or entities become more and more connected, they form increasingly complicated networks and result in complex interactions. The benefit of an interconnected network is that since the information in the network is related in some fashion, information can be exploited to extract latent behaviors or trends of the network which would otherwise be missed if the information is viewed in isolation. We propose the Multi-scale Dynamic Network Graph (MUSING) model to encode, infer and predict the status of dynamic networks by fusing distributed observations (networked data) in the presence of noise or uncertainty. The model structure takes advantage of natural clustering of many networks to facilitate its temporal evolution. The architecture of the scale and time interactions in the MUSING model is particularly amenable to efficient propagation of information. Furthermore, due to the scale nodes in the model, large-scale behaviors and trends of the network are readily available, which offers additional insight into the network status, in addition to the individual nodes.

* information listed above is at the time of submission.

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