Information propagation on modular networks

L Huang, K Park, Y C Lai, Ying-Cheng Lai

Research output: Contribution to journalArticle

51 Citations (Scopus)

Abstract

Networks with a community (or modular) structure underlie many social and biological phenomena. In such a network individuals tend to form sparsely linked local communities, each having dense internal connections. We investigate the dynamics of information propagation on modular networks by using a three-state epidemic model with a unit spreading rate (i.e., the probability for a susceptible individual to be "infected" with the information is one). We find a surprising, resonancelike phenomenon: the information lifetime on the network can be maximized by the number of modules. The result can be useful for optimizing or controlling information spread on social or biological networks.

Original languageEnglish
Article number035103
Pages (from-to)-
Number of pages4
JournalPhysical Review. E, Statistical, Nonlinear and Soft Matter Physics
Volume73
Issue number3
DOIs
Publication statusPublished - Mar 2006

Keywords

  • COMPLEX NETWORKS
  • DYNAMICS
  • VIRUSES
  • SPREAD
  • MODEL

Cite this

Information propagation on modular networks. / Huang, L ; Park, K ; Lai, Y C ; Lai, Ying-Cheng.

In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics, Vol. 73, No. 3, 035103, 03.2006, p. -.

Research output: Contribution to journalArticle

Huang, L ; Park, K ; Lai, Y C ; Lai, Ying-Cheng. / Information propagation on modular networks. In: Physical Review. E, Statistical, Nonlinear and Soft Matter Physics. 2006 ; Vol. 73, No. 3. pp. -.
@article{df2281bb03494c21b46be4f36e3dbf92,
title = "Information propagation on modular networks",
abstract = "Networks with a community (or modular) structure underlie many social and biological phenomena. In such a network individuals tend to form sparsely linked local communities, each having dense internal connections. We investigate the dynamics of information propagation on modular networks by using a three-state epidemic model with a unit spreading rate (i.e., the probability for a susceptible individual to be {"}infected{"} with the information is one). We find a surprising, resonancelike phenomenon: the information lifetime on the network can be maximized by the number of modules. The result can be useful for optimizing or controlling information spread on social or biological networks.",
keywords = "COMPLEX NETWORKS, DYNAMICS, VIRUSES, SPREAD, MODEL",
author = "L Huang and K Park and Lai, {Y C} and Ying-Cheng Lai",
year = "2006",
month = "3",
doi = "10.1103/PhysRevE.73.035103",
language = "English",
volume = "73",
pages = "--",
journal = "Physical Review. E, Statistical, Nonlinear and Soft Matter Physics",
issn = "1539-3755",
publisher = "AMER PHYSICAL SOC",
number = "3",

}

TY - JOUR

T1 - Information propagation on modular networks

AU - Huang, L

AU - Park, K

AU - Lai, Y C

AU - Lai, Ying-Cheng

PY - 2006/3

Y1 - 2006/3

N2 - Networks with a community (or modular) structure underlie many social and biological phenomena. In such a network individuals tend to form sparsely linked local communities, each having dense internal connections. We investigate the dynamics of information propagation on modular networks by using a three-state epidemic model with a unit spreading rate (i.e., the probability for a susceptible individual to be "infected" with the information is one). We find a surprising, resonancelike phenomenon: the information lifetime on the network can be maximized by the number of modules. The result can be useful for optimizing or controlling information spread on social or biological networks.

AB - Networks with a community (or modular) structure underlie many social and biological phenomena. In such a network individuals tend to form sparsely linked local communities, each having dense internal connections. We investigate the dynamics of information propagation on modular networks by using a three-state epidemic model with a unit spreading rate (i.e., the probability for a susceptible individual to be "infected" with the information is one). We find a surprising, resonancelike phenomenon: the information lifetime on the network can be maximized by the number of modules. The result can be useful for optimizing or controlling information spread on social or biological networks.

KW - COMPLEX NETWORKS

KW - DYNAMICS

KW - VIRUSES

KW - SPREAD

KW - MODEL

U2 - 10.1103/PhysRevE.73.035103

DO - 10.1103/PhysRevE.73.035103

M3 - Article

VL - 73

SP - -

JO - Physical Review. E, Statistical, Nonlinear and Soft Matter Physics

JF - Physical Review. E, Statistical, Nonlinear and Soft Matter Physics

SN - 1539-3755

IS - 3

M1 - 035103

ER -