Analysis of Graph Layout Algorithms for Use in Command and Control Network Graphs

Abstract

This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a multiple objective statistics designed to inform on the algorithms performance in both aesthetic characteristics defined in literature, as well as some characteristics defined by the research sponsor. The results suggest that the Dot algorithm performs better with respect to the sponsor defined characteristics, whereas the Fruchterman-Reingold algorithm performs better on aesthetic characteristics.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2020
Accession Number
AD1181199

Entities

People

  • Matthew Stone

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Classification
  • Command And Control
  • Command And Control Systems
  • Computers
  • Data Sets
  • Data Visualization
  • Department Of Defense
  • Engineering
  • Governments
  • Hierarchies
  • Information Science
  • Literature
  • Literature Surveys
  • Neural Networks
  • Simulations
  • Social Networks
  • Statistics
  • United States
  • United States Government
  • Visualizations

Fields of Study

  • Computer science

Readers

  • Business Analytics
  • Computer Vision.
  • Critical Infrastructure Protection in CBRN and WMD Threats.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks
  • Fully Networked C3
  • Fully Networked C3 - Command and Control