Accession Number : AD1009128


Title :   Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks


Descriptive Note : Technical Report,15 Oct 2014,14 Jan 2015


Corporate Author : Imperial College of Science Technology and Medicine London United Kingdom


Personal Author(s) : Perry,Marcus


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1009128.pdf


Report Date : 26 Apr 2016


Pagination or Media Count : 33


Abstract : The final technical report, AFRL-AFOSR-UK-TR-2015-0025, is also available from the DTIC TR repository for more information on this project. The primary focus of this research was to extend the work of Perry et al. [6] by developing a statistical framework that supports the detection of triangle motif-based clusters in complex networks. The specific works accomplished over the 3-month period are as follows: 1. Developed a tractable hypothesis testing framework to assess, a priori, the need for triangle motif-based clustering. 2. Developed an algorithm for clustering undirected networks, where the triangle configuration was used as the basis for forming clusters. 3. Developed a C implementation of the proposed clustering framework.


Descriptors :   OPERATIONS RESEARCH , STATISTICS , ALGORITHMS , clustering , networks


Distribution Statement : APPROVED FOR PUBLIC RELEASE