Predictive Graph Convolutional Networks
Navy STTR 2019.A - Topic N19A-T017
ONR - Mr. Steve Sullivan - email@example.com
Opens: January 8, 2019 - Closes: February 6, 2019 (8:00 PM ET)
TECHNOLOGY AREA(S): Human
Systems, Information Systems
Providentia FNC FY20 Candidate, MAGTF C2, MTC2
algorithms/software to enable assessments and predictive capabilities from
graph data relevant to Naval use cases.
neural networks (CNNs) in recent years have revolutionized computer vision.
Recurrent neural networks have enabled meaningful progress in natural language
processing. Naval data, however, is to a significant extent graph based. For
example, information about an opposing force (e.g., a platform or unit having
some set of attributes/capabilities present at a specific location) is most
effectively captured in a graph form. In the last couple of years, graphical convolutional
networks have been developed with the goal of enabling CNN-based performance on
images to translate to graph data.
PHASE I: Determine
feasibility and complete a proof of concept study of the use of a graphical
convolutional neural network for risk assessment and global trends. Conduct a
detailed analysis of literature and commercial capabilities. For a bounded
number of Reddit sub-groups and GDELT metrics, train a model that predicts one
from the other. Carefully design a validation plan to verify performance of the
resulting model. Develop a Phase II plan with a technology roadmap and
milestones for generalizing the use of their algorithm.
PHASE II: Produce a prototype
system based on the preliminary design from Phase I. Ensure that the capability
of the prototype extends to a machine learning service that can predict the
capabilities/limitations of a platform/force and suggest
opportunities/vulnerabilities from graph-based data. Note: The system will need
to ingest military graphical data at the secret level and provide explanatory
evidence for unit/force assessments; and simulations may be needed to generate
labeled data. During Phase II, the small business may be given specific
mission scenarios by the Government to validate capabilities. An awardee should
assume that the prototype system will need to run as a distributed application
with a mature design for the human computer interface. Deliver a working
prototype of the system (source code and executable) and software documentation
including a user’s manual, and provide a demonstration using a Naval
operational scenario of interest.
PHASE III DUAL USE
APPLICATIONS: Produce a final prototype capable of deployment to training
centers, operational command and control centers and as a virtual application.
Adapt the system to transition as a component to a larger system or as a
standalone commercial product. Provide a means for performance evaluation with
metrics for analysis (e.g., accuracy of assessments) and a method for operator
assessment of product interactions (e.g., display visualizations). The Phase
III system should have an intuitive human computer interface. The software and
hardware should be modified and documented in accordance with guidelines
provided by the engaged Programs of Record and any commercial partners.
Researchers are encouraged to publish S&T contributions.
1. Kipf, Thomas N. “Graph
Convolutional Networks.” 30 September 2016.
2. Ganssle, Graham. “Node
Classification by Graph Convolutional Network.” January 20, 2018.
3. Kipf, Thomas N. and
Willing, Max. “Semi-Supervised Classification with Graph Convolutional
Networks.” ICLR 2017. https://arxiv.org/abs/1609.02907
4. Henaff, Mikael, Bruna,
Joan, LeCun, Yann. “Deep Convolutional Networks on Graph Structured Data.”
Submitted 16 June 2015. https://arxiv.org/abs/1506.05163
5. Li, Yujia, et. Al, “Gated
Graph Sequence Neural Networks” ICLR 2016 https://arxiv.org/abs/1511.05493v4
KEYWORDS: Graph Analysis;
Fusion; Convolutional Neural Networks; Predictive Science; Natural Language