You are here

Predictive Graph Convolutional Networks - 19-008

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N68335-19-C-0310
Agency Tracking Number: N19A-017-0139
Amount: $139,895.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: N19A-T017
Solicitation Number: 19.A
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-06-03
Award End Date (Contract End Date): 2019-12-09
Small Business Information
1818 Library Street Suite 600
Reston, VA 20190
United States
DUNS: 107939233
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Sean Daugherty Sean Daugherty
 Senior Research Scientist
 (703) 326-2919
Business Contact
 Seth Blackwell
Phone: (703) 326-2907
Research Institution
 Northeastern University
 Susan Dorsey Susan Dorsey
360 Huntington Avenue
Boston, MA 02115
United States

 (617) 373-3874
 Nonprofit College or University

Metron and Northeastern University propose to design, develop, and validate a proof-of-concept predictive Graph Convolutional Network (GCN) capability using open source Reddit and GDELT data. We propose: (1) to extract and preprocess open-source Reddit and GDELT data, (2) to design a predictive graph convolutional neural network model, (3) to implement and train that model, and (4) to validate the predictive capability of the model. Northeastern University brings experience developing the state-of-the-art Diffusion Convolutional Recurrent Neural Network (DCRNN) model, a GCN-RNN (Recurrent Neural Network) hybrid for forecasting traffic from a sequence of directed graphs representing historical traffic flows. Metron brings extensive experience in developing, implementing, and validating machine learning models, including GCN and RNN models designed by the Principal Investigator, and transitioning them by integrating them into existing government systems with accompanying interactive user interfaces. In addition to DCRNN, Northeastern University also brings expertise on other GCN and RNN models.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government