On optimising personal network size to manage information flow

  • Authors:
  • Yu-En Lu;Sam Roberts;Tammy M.K. Cheng;Robin Dunbar;Pietro Lió;Jon Crowcroft

  • Affiliations:
  • University of Cambridge, Cambridge, United Kingdom;University of Oxford, Oxford, United Kingdom;University of Cambridge, Cambridge, United Kingdom;University of Oxford, Oxford, United Kingdom;University of Cambridge, Cambridge, United Kingdom;University of Cambridge, Cambridge, United Kingdom

  • Venue:
  • Proceedings of the 1st ACM international workshop on Complex networks meet information & knowledge management
  • Year:
  • 2009

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Abstract

PN refer to the set of ties a specific individual has with other people. There is significant variation in the size of an individual's PN and this paper explores the effect of variation in PN size on information flow through complete social networks. We analyse degree distributions from two personal network datasets and seek to characterise PN size variations. Random matrix analysis is used to demonstrate that the specific mixture of PN sizes plays an important role in shaping the pattern of information dissemination in complete social networks. To explore this further, we conducted a series of studies on normal random graphs that represent social networks in which PN size follows a normal distribution. We demonstrate that there are three critical parameters which influence how information flows through a social network: the mean PN size, the variance in PN size and the rate at which information passes between nodes in the network. The results suggests that if the rate of information flow is increased, for example by using electronic communication rather than face-to-face communication, this could have a dramatic influence on the probability of an individual acquiring a piece of information from a person in their network.