Volume 118, Number 1, April 2017
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||25 May 2017|
Promoting information spreading by using contact memory
1 Web Sciences Center, University of Electronic Science and Technology of China - Chengdu 610054, China
2 Big Data Research Center, University of Electronic Science and Technology of China - Chengdu 610054, China
3 School of Sciences, Xi'an University of Technology - Xi'an 710054, China
4 Center for Polymer Studies and Department of Physics, Boston University - Boston, MA 02215, USA
5 Instituto de Investigaciones Físicas de Mar del Plata (IFIMAR)-Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Mar del Plata-CONICET - Funes 3350, (7600) Mar del Plata, Argentina
Received: 19 March 2017
Accepted: 9 May 2017
Promoting information spreading is a booming research topic in network science community. However, the existing studies about promoting information spreading seldom took into account the human memory, which plays an important role in the spreading dynamics. In this letter we propose a non-Markovian information spreading model on complex networks, in which every informed node contacts a neighbor by using the memory of neighbor's accumulated contact numbers in the past. We systematically study the information spreading dynamics on uncorrelated configuration networks and a group of 22 real-world networks, and find an effective contact strategy of promoting information spreading, i.e., the informed nodes preferentially contact neighbors with a small number of accumulated contacts. According to the effective contact strategy, the high-degree nodes are more likely to be chosen as the contacted neighbors in the early stage of the spreading, while in the late stage of the dynamics, the nodes with small degrees are preferentially contacted. We also propose a mean-field theory to describe our model, which qualitatively agrees well with the stochastic simulations on both artificial and real-world networks.
PACS: 89.75.Hc – Networks and genealogical trees / 87.19.X- – Diseases / 87.23.Ge – Dynamics of social systems
© EPLA, 2017
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