Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

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.

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Document Details

Document Type
Technical Report
Publication Date
Apr 26, 2016
Accession Number
AD1009128

Entities

People

  • Marcus Perry

Organizations

  • Imperial College London

Tags

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Boundaries
  • Computer Networks
  • Data Mining
  • Electronic Mail
  • Information Science
  • Monte Carlo Method
  • Networks
  • Normal Distribution
  • Operations Research
  • Probability
  • Random Variables
  • Simulations
  • Social Media
  • Standards
  • Statistical Tests

Readers

  • Computer Vision.
  • Defense Technology Research and Development.
  • Graph Algorithms and Convex Optimization.