Volume 118, Number 2, April 2017
|Number of page(s)||7|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||23 June 2017|
Predicting the global spread range via small subnetworks
1 School of Data and Computer Science, Sun Yat-sen University - Guangzhou 510006, China
2 School of Electronics and Information Technology, Sun Yat-sen University - Guangzhou 510006, China
3 Key Laboratory of Machine Intelligence and Advanced Computing - Ministry of Education Guangzhou 510006, China
4 Department of ECE, Carnegie Mellon University - Pittsburgh, PA, USA
5 Institute of High Performance Computing, A*STAR - 138632 Singapore
6 Big Data Research Center, University of Electronic Science and Technology of China - Chengdu 611731, China
Received: 15 December 2016
Accepted: 2 June 2017
Modern online social network platforms are replacing traditional media due to their effectiveness in both spreading information and communicating opinions. One of the key problems in these online platforms is to predict the global spread range of any given information. Due to its gigantic size as well as time-varying dynamics, an online social network's global structure, however, is usually inaccessible to most researchers. Thus, it raises the very important issue of how to use solely small subnetworks to predict the global influence. In this paper, based on percolation theory, we show that the global spread range can be predicted well from only two small subnetworks. We test our methods in an artificial network and three empirical online social networks, such as the full Sina Weibo network with 99546027 nodes.
PACS: 89.75.Hc – Networks and genealogical trees / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2017
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