Learning and Inferring Networks from Incomplete Data
Abstract
In an increasingly interconnected world, information, goods, and even diseases propagate across explicit and implicit networks. Depending on the application, network edges can be used to represent different types of connections, from physical wires between computers to friendships among people. And in many situations, one can hope to learn the connections or other properties of a target network by examining whatever outputs of the network are observable, or by smartly intervening in the network and analyzing the results of the intervention. Designing algorithms for learning such networks is the goal of the proposed research. The army applications of this area are numerous and significant. Some examples of problems broadly covered by this proposal include reconstructing an adversarys networks from intercepted or publicly available communications, better understanding supply networks from their disruptions, or even discovering hidden influence structures after observing the votes or writings of politicians or other actors.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 27, 2017
- Accession Number
- AD1058732
Entities
People
- Lev Reyzin
Organizations
- University of Illinois at Chicago