Analysis of communities of interest in data networks

  • Authors:
  • William Aiello;Charles Kalmanek;Patrick McDaniel;Subhabrata Sen;Oliver Spatscheck;Jacobus Van der Merwe

  • Affiliations:
  • Department of Computer Science, University of British Columbia, Vancouver, B.C., Canada;AT&T Labs – Research, Florham Park, NJ;Department of Computer Science and Engineering, Penn State University, University Park, PA;AT&T Labs – Research, Florham Park, NJ;AT&T Labs – Research, Florham Park, NJ;AT&T Labs – Research, Florham Park, NJ

  • Venue:
  • PAM'05 Proceedings of the 6th international conference on Passive and Active Network Measurement
  • Year:
  • 2005

Quantified Score

Hi-index 0.00

Visualization

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.