The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this paper, we introduce the Mixture of Interacting Cascades (MIC), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that MIC achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how MIC can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.

翻译：在线社交平台（如社交网络和社交媒体）的出现，极大地改变了人们理解所接触信息流的方式。在此类平台中，用户间传播的各类信息级联是形成复杂观点动态的主要驱动力，每个用户皆以其特有的行为采纳机制为特征。此外，网络化群体中多重信息或信念的传播很少互不关联。本文提出交互级联混合模型（MIC），该模型基于标记多维霍克斯过程，能够对级联与用户间的非平凡交互进行联合建模。我们着重探究信息级联与用户活动之间的相互作用，并利用时序点过程的混合构建了耦合的用户/级联点过程模型。在合成数据与真实数据上的实验凸显了该方法的优势，并证明MIC在信息级联传播建模方面优于现有方法。最后，我们展示了如何通过MIC学习到的参数，为真实社交网络活动数据提供具有洞察力的双层可视化呈现。