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Anytime Information Cascade Popularity Prediction via Self-Exciting Processes

Xi Zhang · Akshay Aravamudan · Georgios Anagnostopoulos

Hall E #610

Keywords: [ OPT: Stochastic ] [ MISC: Sequential, Network, and Time Series Modeling ] [ APP: Time Series ] [ Miscellaneous Aspects of Machine Learning ]


One important aspect of understanding behaviors of information cascades is to be able to accurately predict their popularity, that is, their message counts at any future time. Self-exciting Hawkes processes have been widely adopted for such tasks due to their success in describing cascading behaviors. In this paper, for general, marked Hawkes point processes, we present closed-form expressions for the mean and variance of future event counts, conditioned on observed events. Furthermore, these expressions allow us to develop a predictive approach, namely, Cascade Anytime Size Prediction via self-Exciting Regression model (CASPER), which is specifically tailored to popularity prediction, unlike existing generative approaches – based on point processes – for the same task. We showcase CASPER’s merits via experiments entailing both synthetic and real-world data, and demonstrate that it considerably improves upon prior works in terms of accuracy, especially for early-stage prediction.

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