Network modularity controls the speed of information diffusion

被引:16
作者
Peng, Hao [1 ]
Nematzadeh, Azadeh [2 ]
Romero, Daniel M. [1 ]
Ferrara, Emilio [3 ]
机构
[1] Univ Michigan, Sch Informat, Ann Arbor, MI 48109 USA
[2] S&P Global, New York, NY 10004 USA
[3] Univ Southern Calif, Informat Sci Inst, Los Angeles, CA 90292 USA
关键词
COMMUNITY STRUCTURE; DYNAMICS; SYSTEMS; TIES; WEAK;
D O I
10.1103/PhysRevE.102.052316
中图分类号
O35 [流体力学]; O53 [等离子体物理学];
学科分类号
070204 ; 080103 ; 080704 ;
摘要
The rapid diffusion of information and the adoption of social behaviors are of critical importance in situations as diverse as collective actions, pandemic prevention, or advertising and marketing. Although the dynamics of large cascades have been extensively studied in various contexts, few have systematically examined the impact of network topology on the efficiency of information diffusion. Here, by employing the linear threshold model on networks with communities, we demonstrate that a prominent network feature-the modular structure-strongly affects the speed of information diffusion in complex contagion. Our simulations show that there always exists an optimal network modularity for the most efficient spreading process. Beyond this critical value, either a stronger or a weaker modular structure actually hinders the diffusion speed. These results are confirmed by an analytical approximation. We further demonstrate that the optimal modularity varies with both the seed size and the target cascade size and is ultimately dependent on the network under investigation. We underscore the importance of our findings in applications from marketing to epidemiology, from neuroscience to engineering, where the understanding of the structural design of complex systems focuses on the efficiency of information propagation.
引用
收藏
页数:8
相关论文
共 43 条
[1]  
Backstrom L., 2006, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, P44, DOI DOI 10.1145/1150402.1150412
[2]  
Bakshy E., 2012, P 21 INT C WORLD WID, P519
[3]   Fast unfolding of communities in large networks [J].
Blondel, Vincent D. ;
Guillaume, Jean-Loup ;
Lambiotte, Renaud ;
Lefebvre, Etienne .
JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT, 2008,
[4]   Complex contagions and the weakness of long ties [J].
Centola, Damon ;
Macy, Michael .
AMERICAN JOURNAL OF SOCIOLOGY, 2007, 113 (03) :702-734
[5]   The Spread of Behavior in an Online Social Network Experiment [J].
Centola, Damon .
SCIENCE, 2010, 329 (5996) :1194-1197
[6]   Can Cascades be Predicted? [J].
Cheng, Justin ;
Adamic, Lada A. ;
Dow, P. Alex ;
Kleinberg, Jon ;
Leskovec, Jure .
WWW'14: PROCEEDINGS OF THE 23RD INTERNATIONAL CONFERENCE ON WORLD WIDE WEB, 2014, :925-935
[7]   On Facebook, Most Ties Are Weak [J].
De Meo, Pasquale ;
Ferrara, Emilio ;
Fiumara, Giacomo ;
Provetti, Alessandro .
COMMUNICATIONS OF THE ACM, 2014, 57 (11) :78-84
[8]   Diffusion on networked systems is a question of time or structure [J].
Delvenne, Jean-Charles ;
Lambiotte, Renaud ;
Rocha, Luis E. C. .
NATURE COMMUNICATIONS, 2015, 6
[9]   Community detection in graphs [J].
Fortunato, Santo .
PHYSICS REPORTS-REVIEW SECTION OF PHYSICS LETTERS, 2010, 486 (3-5) :75-174
[10]   Cascading dynamics in modular networks [J].
Galstyan, Aram ;
Cohen, Paul .
PHYSICAL REVIEW E, 2007, 75 (03)