Analysis of communities of interest in data networks

William Aiello, Charles Kalmanek, Patrick McDaniel, Subhabrata Sen, Oliver Spatscheck, Jacobus Der Van Merwe

Research output: Contribution to journalConference article

22 Scopus citations

Abstract

Communities of interest (COI) have been applied in a variety of environments ranging from characterizing the online buying behavior of individuals to detecting fraud in telephone networks. The common thread among these applications is that the historical COI of an individual can be used to predict future behavior as well as the behavior of other members of the COI. It would clearly be beneficial if COIs can be used in the same manner to characterize and predict the behavior of hosts within a data network. In this paper, we introduce a methodology for evaluating various aspects of COIs of hosts within an IP network. In the context of this study, we broadly define a COI as a collection of interacting hosts. We apply our methodology using data collected from a large enterprise network over a eleven week period. First, we study the distributions and stability of the size of COIs. Second, we evaluate multiple heuristics to determine a stable core set of COIs and determine the stability of these sets over time. Third, we evaluate how much of the communication is not captured by these core COI sets.

Original languageEnglish (US)
Pages (from-to)83-96
Number of pages14
JournalLecture Notes in Computer Science
Volume3431
DOIs
StatePublished - Jan 1 2005
Event6th International Workshop on Passive and Active Network Measurement, PAM 2005 - Boston, MA, United States
Duration: Mar 31 2005Apr 1 2005

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Analysis of communities of interest in data networks'. Together they form a unique fingerprint.

  • Cite this

    Aiello, W., Kalmanek, C., McDaniel, P., Sen, S., Spatscheck, O., & Van Merwe, J. D. (2005). Analysis of communities of interest in data networks. Lecture Notes in Computer Science, 3431, 83-96. https://doi.org/10.1007/978-3-540-31966-5_7