Information can propagate among Online Social Network (OSN) users at a high speed, which makes the OSNs important platforms for viral marketing. Although the viral marketing related problems in OSNs have been extensively studied in the past decade, the existing works all assume known propagation rates. In this paper, we propose a novel model, Dynamic Influence Propagation (DIP), which allows propagation rates to increase after a topic becomes popular and can be used for describing information propagation in OSNs more realistically. Based on DIP, we define a new research problem: Threshold Activation Problem under DIP (TAP-DIP). However, it adds another layer of complexity over the already #P-hard TAP problem. Despite it hardness, we are able to approximate TAP-DIP with $O(\log |V|)$ ratio, where $|V|$ is the number of users in the network. Our solution consists of global optimization techniques and a novel solution to the general version of TAP. We also consider the more complicated case when the propagation rates may change multiple times and the changes are non-immediate, with corresponding solution and analyses. We test our solution using various real OSN datasets, and demonstrate that our solution not only generates high-quality seed sets, but also scales.

中文翻译：

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传播率变化对在线社交网络的影响扩散

信息可以在在线社交网络（OSN）用户之间高速传播，这使OSN成为病毒式营销的重要平台。尽管在过去十年中已广泛研究了OSN中与病毒营销有关的问题，但现有著作都假定传播速率已知。在本文中，我们提出了一种新颖的模型，即动态影响传播（DIP），该模型允许在主题变得流行之后增加传播速率，并可以更实际地用于描述OSN中的信息传播。基于DIP，我们定义了一个新的研究问题：DIP下的阈值激活问题（TAP-DIP）。但是，它在已经是＃P-hard TAP问题的基础上又增加了一层复杂性。尽管硬度高，我们仍可以用$ O（\ log | V |）$ 比例，哪里 $ | V | $是网络中的用户数。我们的解决方案包括全局优化技术和针对TAP常规版本的新颖解决方案。我们还考虑了更为复杂的情况，即传播速率可能会发生多次变化并且变化不是立即的，并提供了相应的解决方案和分析方法。我们使用各种真实的OSN数据集测试了我们的解决方案，并证明了我们的解决方案不仅可以生成高质量的种子集，而且可以缩放。