Accession Number : AD1058732


Title :   Learning and Inferring Networks from Incomplete Data


Descriptive Note : Technical Report,01 Sep 2015,31 Aug 2016


Corporate Author : University of Illinois - Chicago Chicago United States


Personal Author(s) : Reyzin,Lev


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1058732.pdf


Report Date : 27 Jan 2017


Pagination or Media Count : 6


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.


Descriptors :   machine learning , MAXIMUM LIKELIHOOD ESTIMATION , algorithms , SOCIAL NETWORKS , models


Subject Categories : Numerical Mathematics
      Cybernetics


Distribution Statement : APPROVED FOR PUBLIC RELEASE