Department of Computer Science, University of Warwick, Coventry, United Kingdom.
PLoS One. 2018 Jun 28;13(6):e0199845. doi: 10.1371/journal.pone.0199845. eCollection 2018.
The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals. The study of such influence cascades, in particular the identification of influential individuals, has a wide range of applications including epidemic control, viral marketing and the study of social norms. In real-world environments there may be many concepts spreading and interacting. These interactions can affect the spread of a given concept, either boosting it and allowing it to spread further, or inhibiting it and limiting its capability to spread. Previous work does not consider how the interactions between concepts affect concept spread. Taking concept interactions into consideration allows for indirect concept manipulation, meaning that we can affect concepts we are not able to directly control. In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread. We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread.
在一组代理中，概念的传播是一种影响传播形式，可以将其建模为从一组最初激活的个体开始的级联。这种影响级联的研究，特别是有影响力的个体的识别，具有广泛的应用，包括疾病控制、病毒式营销和社会规范的研究。在现实环境中，可能有许多概念在传播和相互作用。这些相互作用会影响给定概念的传播，既可以促进其传播，也可以抑制其传播。以前的工作并没有考虑概念之间的相互作用如何影响概念的传播。考虑到概念之间的相互作用，可以进行间接的概念操作，这意味着我们可以影响我们无法直接控制的概念。在本文中，我们考虑了间接概念操作的问题，并提出了在概念相互作用的环境中间接促进或抑制概念传播的启发式方法。我们定义了一个框架，允许表示任意数量的概念之间的相互作用，并提出了一种启发式方法，旨在识别给定目标概念的重要影响路径，以操纵其传播。我们将这种启发式方法称为最大可能增益的性能与用于操纵影响传播的既定启发式方法进行了比较。
