Hawkes processes with stochastic exogenous effects for continuous-time interaction modelling.

Fan, X; Li, Y; Chen, L; Li, B; Sisson, S

Hawkes processes with stochastic exogenous effects for continuous-time interaction modelling.

Fan, X Li, Y Chen, L

Li, B Sisson, S

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Trans Pattern Anal Mach Intell, 2022, PP, (2), pp. 1848-1861
Issue Date:: 2022-03-23

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Field	Value	Language
dc.contributor.author	Fan, X
dc.contributor.author	Li, Y
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.contributor.author	Li, B
dc.contributor.author	Sisson, S
dc.date.accessioned	2023-03-23T05:06:49Z
dc.date.available	2023-03-23T05:06:49Z
dc.date.issued	2022-03-23
dc.identifier.citation	IEEE Trans Pattern Anal Mach Intell, 2022, PP, (2), pp. 1848-1861
dc.identifier.issn	0162-8828
dc.identifier.issn	1939-3539
dc.identifier.uri	http://hdl.handle.net/10453/168217
dc.description.abstract	Continuous-time interaction data is usually generated under time-evolving environment. Hawkes processes (HP) are commonly used mechanisms for the analysis of such data. However, typical model implementations (such as e.g. stochastic block models) assume that the exogenous (background) interaction rate is constant, and so they are limited in their ability to adequately describe any complex time-evolution in the background rate of a process. In this paper, we introduce a stochastic exogenous rate Hawkes process (SE-HP) which is able to learn time variations in the exogenous rate. The model affiliates each node with a piecewise-constant membership distribution with an unknown number of changepoint locations, and allows these distributions to be related to the membership distributions of interacting nodes. The time-varying background rate function is derived through combinations of these membership functions. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/CE140100049
dc.relation.ispartof	IEEE Trans Pattern Anal Mach Intell
dc.relation.isbasedon	10.1109/TPAMI.2022.3161649
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Hawkes processes with stochastic exogenous effects for continuous-time interaction modelling.
dc.type	Journal Article
utslib.citation.volume	PP
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-23T05:06:48Z
pubs.issue	2
pubs.publication-status	Published online
pubs.volume	PP
utslib.citation.issue	2

Abstract:

Continuous-time interaction data is usually generated under time-evolving environment. Hawkes processes (HP) are commonly used mechanisms for the analysis of such data. However, typical model implementations (such as e.g. stochastic block models) assume that the exogenous (background) interaction rate is constant, and so they are limited in their ability to adequately describe any complex time-evolution in the background rate of a process. In this paper, we introduce a stochastic exogenous rate Hawkes process (SE-HP) which is able to learn time variations in the exogenous rate. The model affiliates each node with a piecewise-constant membership distribution with an unknown number of changepoint locations, and allows these distributions to be related to the membership distributions of interacting nodes. The time-varying background rate function is derived through combinations of these membership functions. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods. We introduce a stochastic gradient MCMC algorithm for efficient, scalable inference. The performance of the SE-HP is explored on real world, continuous-time interaction datasets, where we demonstrate that the SE-HP strongly outperforms comparable state-of-the-art methods.

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http://hdl.handle.net/10453/168217