基于自激励Hawkes过程的微博流行动力学建模与预测
Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes
摘要：
对在线媒体上的单个用户生成的项目的流行动态进行建模和预测的能力在广泛的领域中具有重要意义。在本文中，我们提出了一个概率模型，使用自激霍克斯过程（SEHP）来描述的过程中，个人微博获得他们的知名度。该模型明确地捕获每个转发的触发效应，将其与基于增强泊松过程的模型区分开来，在该模型中，所有先前的转发被简单地聚合为单个触发效应。我们验证了所提出的模型，将其应用于新浪微博，在中国最流行的微博网络。实验结果表明，SEHP模型始终优于基于增强泊松过程的模型。
英文摘要：
The ability to model and predict the popularity dynamics of individual user generated items on online media has important implications in a wide range of areas. In this paper, we propose a probabilistic model using a Self-Excited Hawkes Process(SEHP) to characterize the process through which individual microblogs gain their popularity. This model explicitly captures the triggering effect of each forwarding, distinguishing itself from the reinforced Poisson process based model where all previous forwardings are simply aggregated as a single triggering effect. We validate the proposed model by applying it on Sina Weibo, the most popular microblogging network in China. Experimental results demonstrate that the SEHP model consistently outperforms the model based on reinforced Poisson process.
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。
