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Active and passive diffusion processes in complex networks

机译:复杂网络中的主动和被动扩散过程

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摘要

Ideas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.
机译:观念,信息,病毒:所有这些及其机制,散布在复杂的社会信息中,病毒:我们人际关系描述的所有组织。通常,为了模拟和理解这种复杂现象的发展,使用了通用数学模型。这些模型从模拟扩散的对象开始就不可知论地行动,因此考虑了病毒的扩散,思想和创新。实际上,这种抽象程度使定义可应用于异构上下文的标准工具集变得更加容易。但是,它也可能导致有偏见的,不正确的模拟结果。在这项工作中,我们引入了主动和被动扩散的概念,以区分个人选择影响社交图谱上内容总体传播的程度。从对著名的被动扩散模式Threshold模型(可用于对等压力相关过程进行建模)的分析出发,我们介绍了两种新颖的方法,其目的是提供适用于创新环境的主动模式和混合模式/ ideas扩散模拟。我们在合成和真实数据中进行的分析强调,仅采用被动/主动模型会导致结果冲突,因此强调了需要使用混合方法更好地捕获模拟系统的实际复杂性。

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