病毒式营销分支流程

Ranbir Dhounchak, V. Kavitha, E. Altman
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引用次数: 1

摘要

在研究内容传播时,我们考虑了在线社交网络(osn)中内容出现的内在时间线结构。我们通过适当的多类型分支流程对感兴趣的帖子/内容的传播进行建模。分支过程允许人们从决定局部相互作用的定律和参数中预测全局宏观属性的出现(例如,帖子在网络中的传播)。本地交互在很大程度上取决于时间轴(一个能够容纳许多帖子的反向堆栈,一个专门用于每个用户)结构和用户的朋友数量(即连接)等。我们探索使用多类型分支过程来分析帖子的病毒属性,例如,导出预期的分享数,内容的病毒性概率等。在osn中,新发布的帖子会在时间轴上向下推已有的内容,这对内容的传播影响很大;我们的分析考虑了这种影响。我们发现,当时间轴(TL)结构被忽略时,我们会得出错误的结论:a)例如,即使不太吸引人的帖子也会获得病毒式传播;b)忽略TL结构也表示错误的增长率。更重要的是,人们无法捕捉到一些有趣的范式转换/相变;例如,包括TL影响在内的分析表明,病毒式传播机会与网络活动参数并不是单调的。在最后一部分,我们将在线拍卖整合到我们的病毒式营销模式中。研究了考虑实时竞价的优化问题。我们再次比较了考虑和不考虑不同网络活动水平的TL结构的研究。我们发现,没有TL结构的分析无法捕捉到相关的相变,从而使研究不完整。
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Viral marketing branching processes
1 We consider the inherent timeline structure of the appearance of content in online social networks (OSNs) while studying content propagation. We model the propagation of a post/content of interest by an appropriate multi-type branching process. The branching process allows one to predict the emergence of global macro properties (e.g., the spread of a post in the network) from the laws and parameters that determine local interactions. The local interactions largely depend upon the timeline (an inverse stack capable of holding many posts and one dedicated to each user) structure and the number of friends (i.e., connections) of users, etc. We explore the use of multi-type branching processes to analyze the viral properties of the post, e.g., to derive the expected number of shares, the probability of virality of the content, etc. In OSNs, the new posts push down the existing contents in timelines, which can greatly influence content propagation; our analysis considers this influence. We find that one leads to draw incorrect conclusions when the timeline (TL) structure is ignored: a) for instance, even less attractive posts are shown to get viral; b) ignoring TL structure also indicates erroneous growth rates. More importantly, one cannot capture some interesting paradigm shifts/phase transitions; for example, virality chances are not monotone with network activity parameter, as shown by analysis including TL influence. In the last part, we integrate the online auctions into our viral marketing model. We study the optimization problem considering real-time bidding. We again compared the study with and without considering the TL structure for varying activity levels of the network. We find that the analysis without TL structure fails to capture the relevant phase transitions, thereby making the study incomplete.
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