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)

Citation

Please consider citing the following paper if you find the package helpful to your research

@inproceedings{kong2021evently,
    address = {Jerusalem, Israel},
    author = {Kong, Quyu and Ram, Rohit and Rizoiu, Marian-Andrei},
    booktitle = {ACM International Conference on Web Search and Data Mining (WSDM), Demo},
    title = {{Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes}},
    year = {2021},
}

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_series(par = c(K = 0.9, theta = 1), model_type = 'EXP', Tmax = 5, sim_no = sim_no)
# alternatively, `generate_series` also accepts a model class object
# e.g.
# model <- new_hawkes(par = c(K = 0.9, theta = 1), model_type = 'EXP')
# generate_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

Cascades from real data

We provide a function to help parse provided Tweet JSON objects to cascades. Tweet JSON objects are crawled from the Twitter API and one can refer to the documentation for more information.

In this package, we provide a collection of tweets about the same topic for demonstration purpose. The file is in a jsonl format where each line of this file is a Tweet JSON string. The content of the tweets have been encrypted due to the Twitter API agreement. One can find the file at inst/extdata/tweets_anonymized.jsonl. Let’s now extract cascades from the file.

filepath <- system.file('extdata', 'tweets_anonymized.jsonl', package = 'evently')
cascades <- parse_raw_tweets_to_cascades(filepath, progress = F)
print(cascades[seq(3)])
## [[1]]
##   magnitude time
## 1       337    0
## 
## [[2]]
##   magnitude time
## 2       130    0
## 
## [[3]]
##   magnitude time
## 3       251    0

As the file includes all raw tweets, it is natural to see that most cascades extracted only have single events in it. We can further fit Hawkes models on these cascades.

Available models

There are 8 models available so far in this package:

Model Abbreviation (model_type) Intensity Function Parameters
Hawkes process with an exponential kernel function EXP K,theta
Hawkes process with a power-law kernel function PL K,c,theta
HawkesN process with an exponential kernel function EXPN K,theta,N
HawkesN process with a power-law kernel function PLN K,c,theta,N
Marked Hawkes process with an exponential kernel function mEXP K,beta,theta
Marked Hawkes process with a power-law kernel function mPL K,beta,c,theta
Marked HawkesN process with an exponential kernel function mEXPN K,beta,theta,N
Marked HawkesN process with a power-law kernel function mPLN 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 or .

Documentation

Please consult the package documentation for more details and tutorials

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.