Theories for influencer identification in complex networks
Theories for influencer identification in complex networks
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DOI：
10.1007/978-3-319-77332-2_8
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发表时间：
2017-07
期刊：
影响因子：
--
通讯作者：
S. Pei;F. Morone;H. Makse
中科院分区：
文献类型：
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作者：
S. Pei;F. Morone;H. Makse
在社会和生物系统中，相互作用网络的结构异质性导致在一系列动态过程中出现一小部分有影响力的节点或影响者。尽管这些影响者比整个网络小得多，但据观察，他们能够在不同的背景下塑造大量人口的集体动态。因此，成功识别影响者应该对各种现实世界的传播动态具有深远的影响，例如病毒营销、流行病爆发和连锁失败。在本章中，我们首先总结了基于中心性的方法在复杂网络中寻找单个影响者的方法，然后从集体的角度讨论了更复杂的定位多个影响者的问题。将介绍植根于集体影响理论、信仰传播和计算机科学的进展。最后，我们介绍了影响者识别在不同的现实系统中的一些应用，包括在线社交平台、科学出版物、大脑网络和社会经济系统。
In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks, and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation, and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks, and socioeconomic systems.