Accession Number:

ADA626905

Title:

Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

Descriptive Note:

Final rept. 15 Oct 2014-14 Jan 2015

Corporate Author:

IMPERIAL COLL OF SCIENCE TECHNOLOGY AND MEDICINE LONDON (UNITED KINGDOM)

Personal Author(s):

Report Date:

2015-03-16

Pagination or Media Count:

30.0

Abstract:

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 accomplis hed over the 3-month period are as follows 1. Developed a tractable hypothesis testing framework to as sess, 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:

  • Statistics and Probability
  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE