Trend-driven information cascades on random networks
Trend-driven information cascades on random networks
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DOI：
10.1103/physreve.92.062823
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发表时间：
2015-12-21
影响因子：
2.4
通讯作者：
Kobayashi, Teruyoshi
中科院分区：
文献类型：
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作者：
Kobayashi, Teruyoshi
全球级联的门槛模型已被广泛用于模拟现实世界的集体行为，例如时尚的传染性传播和新技术的采用。这些级联模型的一个共同属性是，极小的种子部分可以通过局部感染传播到无限大网络的有限部分。然而，在社会和经济网络中，个人的行为往往不仅受到他们的直接邻居正在做的事情的影响，而且还受到大多数人作为一种趋势正在做的事情的影响。趋势影响个人的行为，而个人的行为创造趋势。为了分析局部和全球尺度现象之间的复杂相互作用，我通过引入一种称为全局节点(或趋势跟随者)的节点来推广标准阈值模型，该节点的激活概率取决于全球尺度趋势，特别是激活节点在种群中的百分比。该模型表明，当趋势出现时，全局节点起到加速级联的作用，同时降低趋势出现的概率。因此，全球节点要么促进，要么抑制级联，这表明适度的趋势追随者份额可能会使级联的平均大小最大化。
Threshold models of global cascades have been extensively used to model real-world collective behavior, such as the contagious spread of fads and the adoption of new technologies. A common property of those cascade models is that a vanishingly small seed fraction can spread to a finite fraction of an infinitely large network through local infections. In social and economic networks, however, individuals' behavior is often influenced not only by what their direct neighbors are doing, but also by what the majority of people are doing as a trend. A trend affects individuals' behavior while individuals' behavior creates a trend. To analyze such a complex interplay between local- and global-scale phenomena, I generalize the standard threshold model by introducing a type of node called global nodes (or trend followers), whose activation probability depends on a global-scale trend, specifically the percentage of activated nodes in the population. The model shows that global nodes play a role as accelerating cascades once a trend emerges while reducing the probability of a trend emerging. Global nodes thus either facilitate or inhibit cascades, suggesting that a moderate share of trend followers may maximize the average size of cascades.