是病毒式传播还是广播式传播？描述级联的病毒性和增长
Go viral or go broadcast? Characterizing the virality and growth of cascades
摘要：
量化级联的病毒性是一个跨学科的重要问题，如疾病的传播，信息的传播和创新的传播。一个合适的病毒式传播度量应该能够区分浅层的、广播式的传播过程和深层的、多代的分支过程。虽然已有一些有价值的工作致力于这一领域，其中大多数没有考虑的扩散源的位置，这使得他们陷入图同构的陷阱，并会导致不精确的估计级联病毒性在某些情况下不可避免的。在本文中，我们提出了一个根感知的方法来量化的级联的病毒性适当考虑扩散树中的根节点。与合成和经验级联的应用程序，我们展示了拟议的病毒性措施的性质和潜在的效用。基于优先附着机制，我们进一步引入了一个模型来模拟级联的生长。所提出的模型，使广播和病毒传播之间的插值级联的增长过程中。通过数值模拟，我们证明了所提出的模型的有效性，在表征不断增长的级联病毒。我们的工作有助于理解级联病毒和增长，并可能在一系列政策领域，包括病毒营销，传染病和信息传播提供实际意义。
英文摘要：
Quantifying the virality of cascades is an important question across disciplines such as the transmission of disease, the spread of information and the diffusion of innovations. An appropriate virality metric should be able to disambiguate between a shallow, broadcast-like diffusion process and a deep, multi-generational branching process. Although several valuable works have been dedicated to this field, most of them fail to take the position of the diffusion source into consideration, which makes them fall into the trap of graph isomorphism and would result in imprecise estimation of cascade virality inevitably under certain circumstances. In this paper, we propose a root-aware approach to quantifying the virality of cascades with proper consideration of the root node in a diffusion tree. With applications on synthetic and empirical cascades, we show the properties and potential utility of the proposed virality measure. Based on preferential attachment mechanisms, we further introduce a model to mimic the growth of cascades. The proposed model enables the interpolation between broadcast and viral spreading during the growth of cascades. Through numerical simulations, we demonstrate the effectiveness of the proposed model in characterizing the virality of growing cascades. Our work contributes to the understanding of cascade virality and growth, and could offer practical implications in a range of policy domains including viral marketing, infectious disease and information diffusion.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
