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Title: Heuristic Strategies for Persuader Selection in Contagions on Complex Networks
Authors: Wang, Peng
Zhang, Li-Jie
Xu, Xin-Jian
Xiao, Gaoxi
Keywords: Centrality
Issue Date: 2017
Source: Wang, P., Zhang, L.-J., Xu, X.-J., & Xiao, G. (2017). Heuristic Strategies for Persuader Selection in Contagions on Complex Networks. PLOS ONE, 12(1), e0169771-.
Series/Report no.: PLOS ONE
Abstract: Individual decision to accept a new idea or product is often driven by both self-adoption and others’ persuasion, which has been simulated using a double threshold model [Huang et al., Scientific Reports 6, 23766 (2016)]. We extend the study to consider the case with limited persuasion. That is, a set of individuals is chosen from the population to be equipped with persuasion capabilities, who may succeed in persuading their friends to take the new entity when certain conditions are satisfied. Network node centrality is adopted to characterize each node’s influence, based on which three heuristic strategies are applied to pick out persuaders. We compare these strategies for persuader selection on both homogeneous and heterogeneous networks. Two regimes of the underline networks are identified in which the system exhibits distinct behaviors: when networks are sufficiently sparse, selecting persuader nodes in descending order of node centrality achieves the best performance; when networks are sufficiently dense, however, selecting nodes with medium centralities to serve as the persuaders performs the best. Under respective optimal strategies for different types of networks, we further probe which centrality measure is most suitable for persuader selection. It turns out that for the first regime, degree centrality offers the best measure for picking out persuaders from homogeneous networks; while in heterogeneous networks, betweenness centrality takes its place. In the second regime, there is no significant difference caused by centrality measures in persuader selection for homogeneous network; while for heterogeneous networks, closeness centrality offers the best measure.
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0169771
Rights: © 2017 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
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