The Annals of Applied Probability

Hawkes processes on large networks

Sylvain Delattre, Nicolas Fournier, and Marc Hoffmann

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We generalise the construction of multivariate Hawkes processes to a possibly infinite network of counting processes on a directed graph $\mathbb{G}$. The process is constructed as the solution to a system of Poisson driven stochastic differential equations, for which we prove pathwise existence and uniqueness under some reasonable conditions.

We next investigate how to approximate a standard $N$-dimensional Hawkes process by a simple inhomogeneous Poisson process in the mean-field framework where each pair of individuals interact in the same way, in the limit $N\rightarrow\infty$. In the so-called linear case for the interaction, we further investigate the large time behaviour of the process. We study in particular the stability of the central limit theorem when exchanging the limits $N,T\rightarrow\infty$ and exhibit different possible behaviours.

We finally consider the case $\mathbb{G}=\mathbb{Z}^{d}$ with nearest neighbour interactions. In the linear case, we prove some (large time) laws of large numbers and exhibit different behaviours, reminiscent of the infinite setting. Finally, we study the propagation of a single impulsion started at a given point of $\mathbb{Z}^{d}$ at time $0$. We compute the probability of extinction of such an impulsion and, in some particular cases, we can accurately describe how it propagates to the whole space.

Article information

Ann. Appl. Probab., Volume 26, Number 1 (2016), 216-261.

Received: March 2014
Revised: November 2014
First available in Project Euclid: 5 January 2016

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Zentralblatt MATH identifier

Primary: 60F05: Central limit and other weak theorems 60G55: Point processes 60G57: Random measures

Point processes multivariate Hawkes processes stochastic differential equations limit theorems mean-field approximations interacting particle systems


Delattre, Sylvain; Fournier, Nicolas; Hoffmann, Marc. Hawkes processes on large networks. Ann. Appl. Probab. 26 (2016), no. 1, 216--261. doi:10.1214/14-AAP1089.

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