Toward Large-Graph Comparison Measures to Understand Internet Topology Dynamics

Abstract

By measuring network changes, we can get a better understanding of a network. Extending this to the Internet, we are able to understand the constantly occurring changes on an international scale. In this research, we propose a measure that conveys the relative magnitude of the change between two networks (i.e., Internet topology). The measure is normalised and intuitively gives an indication of whether the change is small or large. We start off by applying this measure to standard common graphs, as well as random graphs. These graphs were first simulated and the measurements taken; results were then proved theoretically. These corresponded to the simulation results, thus demonstrating correctness. For case studies, we compared actual implemented networks with that which is inferred by probes. This comparison was done to study how accurate the probes were in discovering actual network topology. Finally, we conducted real-world experiments by applying the measurements to certain segments of the Internet. We observed that the measurements indeed do pick up events which significantly influenced structural changes to the Internet.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2013
Accession Number
ADA589764

Entities

People

  • Lee H. Daryl

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Energy and Power Technologies
  • Engineered Resilient Systems

DTIC Thesaurus Topics

  • Applied Mathematics
  • Autonomous Systems
  • Case Studies
  • Computer Networks
  • Data Analysis
  • Graph Theory
  • Infrastructure
  • Network Protocols
  • Network Science
  • Network Topology
  • Political Movements
  • Probability
  • Probability Distributions
  • Routing Protocols
  • Standards
  • Statistical Analysis
  • Statistics

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference