Anytime Information Cascade Popularity Prediction via Self-Exciting Processes
Xi Zhang, Akshay Aravamudan, Georgios C Anagnostopoulos
Abstract
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.
BibTeX
@InProceedings{pmlr-v162-zhang22a,
title = {Anytime Information Cascade Popularity Prediction via Self-Exciting Processes},
author = {Zhang, Xi and Aravamudan, Akshay and Anagnostopoulos, Georgios C},
booktitle = {Proceedings of the 39th International Conference on Machine Learning},
pages = {26028--26047},
year = {2022},
editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan},
volume = {162},
series = {Proceedings of Machine Learning Research},
month = {17--23 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v162/zhang22a/zhang22a.pdf},
url = {https://proceedings.mlr.press/v162/zhang22a.html},
abstract = {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.}
}