Accession Number : AD1020605


Title :   Multi-modal Social Networks: A MRF Learning Approach


Descriptive Note : Technical Report,01 Sep 2011,31 Aug 2014


Corporate Author : University of Texas at Austin Austin United States


Personal Author(s) : Shakkottai,Sanjay ; Sanghavi,Sujay


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1020605.pdf


Report Date : 20 Jun 2016


Pagination or Media Count : 9


Abstract : The work primarily focused on two lines of research.1. We propose new greedy algorithms for learning the structure of a graphical model of a probability distribution, given samples drawn fromthe distribution. Our research modifies greedy algorithms through appropriate node pruning, to result in fast algorithms that provideanalytical guarantees on correctness.2. The objective of this line of work is to use noisy measurements from cascades stochastic processes for spread on graphs to learn thespread of information / opinion / malware. Our approach for this learning problem has been to view this as hypothesis testing on graphs given noisy and partial information on both node states and the network graph, we formulate the problem as distinguishing between a benignhypothesis (no spreading process) and a malicious hypothesis (spreading process such as malware). This approach has been used in asequence of studies, starting from distinguishing with partial information, to that with nodes with are adversarial (nodes could lie about theirstate), to dealing with noisy network knowledge. We have also been able to use this approach to learn the identity of communities withshared interests.


Descriptors :   algorithms , social networks , probability distributions , stochastic processes , malware


Subject Categories : Statistics and Probability
      Operations Research
      Sociology and Law
      Computer Systems


Distribution Statement : APPROVED FOR PUBLIC RELEASE