Modeling Events with Cascades of Poisson Processes
This work addresses the problem of scalable event modeling for researchers and practitioners in fields like social media analysis and collaborative editing, but it is incremental as it builds on existing Poisson process frameworks.
The authors tackled the problem of modeling events in continuous time by proposing a probabilistic model where each event triggers a Poisson process of successor events, representing observed events as a superposition of Poisson processes, and they applied this to Twitter messages and Wikipedia revision history, achieving efficient inference with an EM algorithm that can be distributed for large datasets.
We present a probabilistic model of events in continuous time in which each event triggers a Poisson process of successor events. The ensemble of observed events is thereby modeled as a superposition of Poisson processes. Efficient inference is feasible under this model with an EM algorithm. Moreover, the EM algorithm can be implemented as a distributed algorithm, permitting the model to be applied to very large datasets. We apply these techniques to the modeling of Twitter messages and the revision history of Wikipedia.