Visualizing Spatial Dependencies in Network Topology

Abstract

Increasingly, the data available to network analysts includes not only relationships between entities but the observation of entity attributes and relations in geographic space. Integrating this information with existing dynamic network analysis techniques demands new models and new tools. This paper introduces extensions to the ORA dynamic network analysis platform intended to meet this need. In particular, we present new visualization techniques for displaying the network topology of large, noisy datasets embedded in geographic space. We present these extensions and demonstrate them on some sample datasets.

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Document Details

Document Type
Technical Report
Publication Date
Jul 12, 2010
Accession Number
ADA525370

Entities

People

  • Jamie Olson
  • Kathleen Carley

Tags

Communities of Interest

  • Energy and Power Technologies
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Algorithms
  • Clustering
  • Computer Science
  • Computers
  • Data Sets
  • Detectors
  • Grids
  • High Resolution
  • Identification
  • Identification Systems
  • Military Research
  • Network Topology
  • Random Variables
  • Social Networks
  • Statistical Tests
  • Statistics
  • Topology

Fields of Study

  • Computer science

Readers

  • Computer Networking
  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Software Engineering.

Technology Areas

  • Space