In this paper, we propose a survival factorization framework that models information cascades by tying together social influence patterns, topical structure, and temporal dynamics. This is achieved through the introduction of a latent space which encodes: (a) the relevance of an information cascade on a topic; (b) the topical authoritativeness and the susceptibility of each individual involved in the information cascade, and (c) temporal topical patterns. By exploiting the cumulative properties of the survival function and of the likelihood of the model on a given adoption log, which records the observed activation times of users and side-information for each cascade, we show that the inference phase is linear in the number of users and in the number of adoptions. The evaluation on both synthetic and real-world data shows the effectiveness of the model in detecting the interplay between topics and social influence patterns, which ultimately provides high accuracy in predicting users activation times.

中文翻译：

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扩散网络生存分析的分解方法

在本文中，我们提出了一个生存因子分解框架，该框架通过将社会影响模式、主题结构和时间动态联系在一起来对信息级联进行建模。这是通过引入潜在空间来实现的，该空间编码：（a）信息级联与主题的相关性；(b) 参与信息级联的每个人的主题权威性和敏感性，以及 (c) 时间主题模式。通过利用生存函数的累积特性和模型在给定采用日志上的似然性（记录观察到的用户激活时间和每个级联的辅助信息），我们表明推理阶段在数量上是线性的用户和收养数量。