Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

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.

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Document Details

Document Type
Technical Report
Publication Date
Mar 16, 2015
Accession Number
ADA626905

Entities

People

  • Marcus B. Perry

Organizations

  • Imperial College London

Tags

DTIC Thesaurus Topics

  • Air Force Research Laboratories
  • Algorithms
  • Communities
  • Data Mining
  • Detection
  • Information Science
  • Monte Carlo Method
  • Normal Distribution
  • Operations Research
  • Probability
  • Random Variables
  • Simulations
  • Social Media
  • Standards
  • Statistical Tests
  • Triangles
  • Urban Areas

Readers

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