evently: simulation, fitting of Hawkes processes

Introduction

This package is designed for simulating and fitting the Hawkes processes and the HawkesN processes with several options of kernel functions. Currently, it assumes univariate processes without background event rates. Prior knowledge about the models is assumed in the following tutorial and please refer to [1] and [2] for details about the models.

library(evently)

Installation and dependencies

Several dependencies (poweRlaw, AMPL, Ipopt) are required for running this package. These dependencies will be installed automatically by R or by following instructions upon package load.

Install the package by executing

if (!require('devtools')) install.packages('devtools')
devtools::install_github('behavioral-ds/evently')

Simulating cascades

Let’s first simulate 100 event cascades of the Hawkes process with an exponential kernel function (please refer to the Available models for models and their abbreviations in the package) with a given parameter set, \\kappa = 0.9, \\theta= 1. For each simulation, we only simulate until 5 seconds. The resulted cascades are placed in a single list where each cascade is a data.frame.

set.seed(4)
sim_no <- 100
data <- generate_hawkes_event_series(par = c(K = 0.9, theta = 1), model_type = 'EXP', Tmax = 5, sim_no = sim_no)
# alternatively, `generate_hawkes_event_series` also accepts a model class object
# e.g.
# model <- new_hawkes_model(par = c(K = 0.9, theta = 1), model_type = 'EXP')
# generate_hawkes_event_series(model = model, Tmax = 5, sim_no = sim_no)

head(data[[1]])
##   magnitude      time
## 1         1 0.0000000
## 2         1 0.5941959
## 3         1 1.4712411
## 4         1 1.6105430
## 5         1 1.7855535
## 6         1 1.8883869

A simulated process is represented by a data.frame where each row is an event. time indicates the event happening time, while magnitude is the event mark information which is always 1 if model_type is an unmarked model. In the context of retweet diffusion cascades, the first row is the original tweet and all following events are its retweets. time records the relative time (in second) of each retweet to the original tweet and magnitude is the follows’ count of the user who retweeted.

Fitting a model on data

We can then fit on the cascades simulated in the previous section. After providing the data and model_type, the fitting procedure will spawn 10 AMPL optimization procedures with different parameter inistializations due to the non-convexity of some likelihood functions. Among the 10 fitted model, the one giving the best likelihood value will be returned. To make the fitting procedure faster, we can specify the number of cores to be used for fitting them in parallel.

fitted_model <- fit_series(data, model_type = 'EXP', observation_time = 5, cores = 10)

fitted_model
## Model: EXP 
## No. of cascades: 100 
## init_par
##   K 7.92e+00; theta 1.32e+00
## par
##   K 8.51e-01; theta 1.06e+00
## Neg Log Likelihood: 285.488 
## lower_bound
##   K 1.00e-100; theta 1.00e-100
## upper_bound
##   K 1.00e+04; theta 3.00e+02
## convergence: 0

Available models

There are 8 models available so far in this package:

ModelAbbreviation (model_type)Intensity FunctionParameters
Hawkes process with an exponential kernel functionEXP\\kappa\\sum\_{t\_i \< t} \\theta e^{-\\theta (t-t\_i)}K,theta
Hawkes process with a power-law kernel functionPL\\kappa\\sum\_{t\_i \< t} (t-t\_i + c)^{-(1+\\theta)}K,c,theta
HawkesN process with an exponential kernel functionEXPN\\kappa\\frac{N-N\_t}{N}\\sum\_{t\_i \< t} \\theta e^{-\\theta (t-t\_i)}K,theta,N
HawkesN process with a power-law kernel functionPLN\\kappa\\frac{N-N\_t}{N}\\sum\_{t\_i \< t} (t-t\_i + c)^{-(1+\\theta)}K,c,theta,N
Marked Hawkes process with an exponential kernel functionmEXP\\kappa\\sum\_{t\_i \< t} \\theta m\_i^{\\beta} e^{-\\theta (t-t\_i)}K,beta,theta
Marked Hawkes process with a power-law kernel functionmPL\\kappa\\sum\_{t\_i \< t} m\_i^{\\beta} (t-t\_i + c)^{-(1+\\theta)}K,beta,c,theta
Marked HawkesN process with an exponential kernel functionmEXPN\\kappa\\frac{N-N\_t}{N}\\sum\_{t\_i \< t} \\theta m\_i^{\\beta} e^{-\\theta (t-t\_i)}K,beta,theta,N
Marked HawkesN process with a power-law kernel functionmPLN\\kappa\\frac{N-N\_t}{N}\\sum\_{t\_i \< t} m\_i^{\\beta}(t-t\_i + c)^{-(1+\\theta)}K,beta,c,theta,N

Acknowledgement

The development of this package is supported by the Green Policy grant from the National Security College, Crawford School, ANU.

License

Both dataset and code are distributed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. If you require a different license, please contact us at [email protected] or [email protected].

Reference

[1] Rizoiu, M. A., Lee, Y., Mishra, S., & Xie, L. (2017, December). Hawkes processes for events in social media. In Frontiers of Multimedia Research (pp. 191-218). Association for Computing Machinery and Morgan & Claypool.
[2] Rizoiu, M. A., Mishra, S., Kong, Q., Carman, M., & Xie, L. (2018, April). SIR-Hawkes: Linking epidemic models and Hawkes processes to model diffusions in finite populations. In Proceedings of the 2018 World Wide Web Conference (pp. 419-428). International World Wide Web Conferences Steering Committee.
[3] Mishra, S., Rizoiu, M. A., & Xie, L. (2016, October). Feature driven and point process approaches for popularity prediction. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management (pp. 1069-1078). ACM.
[4] Kong, Q., Rizoiu, M. A., & Xie, L. (2019). Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models. arXiv preprint arXiv:1910.05451.

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Quyu Kong
PhD student

I am a Ph.D. student from the Computational Media Lab at the Research School of Computer Science of The Australian National University and affiliated with Data61, CSIRO. I am supervised by Dr Marian-Andrei Rizoiu, Prof Lexing Xie and Dr Stephen Wan. My current research focuses on modeling information diffusion in social media. Specifically, I am working on understanding the connection between epidemic models and self-exciting point processes and quantifying online diffusions with tools from the connection.

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