Uncovering Social Network Activity Using Joint User and Topic Interaction
This work addresses the challenge of understanding complex dynamics in social networks for researchers and practitioners, though it appears incremental as it builds on existing Hawkes process models.
The paper tackles the problem of modeling correlated information cascades and user interactions in social networks by introducing the Mixture of Interacting Cascades (MIC) model, which achieves superior performance over existing methods in experiments on synthetic and real data.
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.