Promoting information spreading by using contact memory

被引:32
作者
Gao, Lei [1 ,2 ]
Wang, Wei [1 ,2 ]
Shu, Panpan [3 ]
Gao, Hui [1 ,2 ]
Braunstein, Lidia A. [4 ,5 ,6 ]
机构
[1] Univ Elect Sci & Technol China, Web Sci Ctr, Chengdu 610054, Peoples R China
[2] Univ Elect Sci & Technol China, Big Data Res Ctr, Chengdu 610054, Peoples R China
[3] Xian Univ Technol, Sch Sci, Xian 710054, Peoples R China
[4] Boston Univ, Ctr Polymer Studies, Boston, MA 02215 USA
[5] Boston Univ, Dept Phys, 590 Commonwealth Ave, Boston, MA 02215 USA
[6] Univ Mar del Plata, Fac Ciencias Exactas & Nat, Inst Invest Fis Mar del Plata IFIMAR, Dept Fis,CONICET, Funes 3350, RA-7600 Mar Del Plata, Argentina
基金
中国国家自然科学基金;
关键词
NETWORKS; DYNAMICS; IMPACT; NODES;
D O I
10.1209/0295-5075/118/18001
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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 highdegree 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. Copyright (C) EPLA, 2017
引用
收藏
页数:7
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