research-article

Modeling Sparse Information Diffusion at Scale via Lazy Multivariate Hawkes Processes

Authors:

Maximilian Nickel,

Matthew LeAuthors Info & Claims

WWW '21: Proceedings of the Web Conference 2021

Pages 706 - 717

https://doi.org/10.1145/3442381.3450094

Published: 03 June 2021 Publication History

Abstract

Multivariate Hawkes Processes (MHPs) are an important class of temporal point processes that have enabled key advances in understanding and predicting social information systems. However, due to their complex modeling of temporal dependencies, MHPs have proven to be notoriously difficult to scale, what has limited their applications to relatively small domains. In this work, we propose a novel model and computational approach to overcome this important limitation. By exploiting a characteristic sparsity pattern in real-world diffusion processes, we show that our approach allows to compute the exact likelihood and gradients of an MHP – independently of the ambient dimensions of the underlying network. We show on synthetic and real-world datasets that our method does not only achieve state-of-the-art modeling results, but also improves runtime performance by multiple orders of magnitude on sparse event sequences. In combination with easily interpretable latent variables and influence structures, this allows us to analyze diffusion processes in networks at previously unattainable scale.

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cover image ACM Conferences

WWW '21: Proceedings of the Web Conference 2021

April 2021

4054 pages

ISBN:9781450383127

DOI:10.1145/3442381

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
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Published: 03 June 2021

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April 19 - 23, 2021

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Cited By

Calderon PSoen ARizoiu M(2025)Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored DataIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.348611712:1(25-37)Online publication date: Feb-2025
https://doi.org/10.1109/TCSS.2024.3486117
Agarwal AUsunier NLazaric ANickel M(2024)System-2 Recommenders: Disentangling Utility and Engagement in Recommendation Systems via Temporal Point-ProcessesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659004(1763-1773)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3659004
Chen ZXu BCai TYang ZLiao X(2023)A Dynamic Emotional Propagation Model over Time for Competitive EnvironmentsElectronics10.3390/electronics1224493712:24(4937)Online publication date: 8-Dec-2023
https://doi.org/10.3390/electronics12244937
Qu ZLyu CChi C(2022)MUSH: Multi-Stimuli Hawkes Process Based Sybil Attacker Detector for User-Review Social NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2022.318651319:4(4600-4614)Online publication date: Dec-2022
https://doi.org/10.1109/TNSM.2022.3186513
Liu YGao JZhao ZLi JYue Z(2022)Understanding information diffusion with psychological field dynamicInformation Processing and Management: an International Journal10.1016/j.ipm.2022.10295659:4Online publication date: 1-Jul-2022
https://dl.acm.org/doi/10.1016/j.ipm.2022.102956

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