Volume 119, Number 1, July 2017
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||07 September 2017|
Identifying multiple influential spreaders via local structural similarity
1 Data Science and Cloud Service Centre, Shanghai University of Finance and Economics - Shanghai 200433, PRC
2 Research Center of Complex Systems Science, University of Shanghai for Science and Technology Shanghai 200093, PRC
3 College of Humanities, Shanghai University of Finance and Economics - Shanghai 200433, PRC
Received: 28 April 2017
Accepted: 9 August 2017
Identifying the nodes with largest spreading influence is of significance for information diffusion, product exposure and contagious disease detection. In this letter, based on the local structural similarity, we present a method (LSS) to identify the multiple influential spreaders. Firstly, we choose the node with the largest degree as the first spreader. Then the new spreaders would be selected if they belong to the first- or second-order–neighbor node set of the spreaders and their local structural similarities with other spreaders are smaller than the threshold parameter r. Comparing with the susceptible-infected-recovered model, the experimental results for four empirical networks show that the spreading influences of spreaders selected by the local structural similarity method are larger than that of the color method, the degree, betweenness and closeness centralities. The further experimental results for the Barabàsi-Albert and random networks show that the LSS method could identify the multiple influential spreaders more accurately, which suggests that the local structural property plays a more important role than the distance for identifying multiple influential spreaders.
PACS: 89.75.Fb – Structures and organization in complex systems / 87.15.A- – Theory, modeling, and computer simulation
© EPLA, 2017
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.