用联合用户与话题互动模型，揭示社交网络中信息传播的深层规律。
Uncovering Social Network Activity Using Joint User and Topic Interaction
- 构建混合交互级联模型，联合建模用户行为与信息传播
- 在真实和合成数据上显著优于现有方法
- 可生成双层可视化图谱，直观展现社交活动动态
在线社交平台的兴起深刻改变了人们接收信息的方式。在这些平台中，用户间的信息级联是推动观点形成复杂动态的核心力量，每个用户具有独特的行为采纳机制。多个信息或信念在网络中传播时通常并非独立。本文提出混合交互级联（Mixture of Interacting Cascades, MIC）模型，一种带有标记的多维霍克斯过程，能够联合建模级联间与用户间的非平凡交互。通过混合时间点过程构建耦合的用户/级联点过程模型，强调信息级联与用户活跃度之间的相互作用。在合成数据与真实数据上的实验表明，MIC在建模信息传播方面优于现有方法。最后，我们展示了如何利用学习到的参数，对真实社交网络活动数据进行有意义的双层可视化。
原文摘要 · Abstract (English)
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.
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