Abstract
Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.
Highlights
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large- Scale Social Networks
In viral marketing, advertising a small group of influential customers to adopt a new product can inexpensively trigger a large scale of further adoption[1,2,3,4]; in epidemics control, the immunization of structurally important persons can efficiently halt global epidemic outbreaks in contact networks[8,9,10,11]; and in biological systems like brain networks, some significant nodes are responsible for broadcasting information and locating and protecting them are crucial for the whole information processing system[12]
Through comparisons with competing heuristic methods, including high degree (HD)[7,8], adaptive high degree (HDA)[24], PageRank (PR)[18] and k-core[19,20,21,22], we find that the set of spreaders selected by Collective Influence (CI) can exert larger collective influence on the population with the same number of initial seeds
Summary
Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large- Scale Social Networks. Local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community. All of them are established in the non-interacting setting, where nodes’ significance is evaluated by taking them as isolated agents As a result, these ad-hoc approaches, designed for finding single superspreaders, fail to provide the optimal solution for the general case of multiple influencers. The over-simplified spreading models usually neglect such important factors as activity frequency[26], connection strength and behavioral preferences, fail to reproduce some observed characteristics www.nature.com/scientificreports/
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