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):

Report Date:

2016-04-26

Pagination or Media Count:

33.0

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.

Subject Categories:

Distribution Statement:

APPROVED FOR PUBLIC RELEASE