This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix
arXiv Fulltext PDF:C\:\\Users\\Jan\\Zotero\\storage\\B73CE36V\\Rizoiu et al. - 2017 - A Tutorial on Hawkes Processes for Events in Socia.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\Jan\\Zotero\\storage\\HCXGBSM4\\1708.html:text/html
%0 Journal Article
%1 rizoiu_tutorial_2017
%A Rizoiu, Marian-Andrei
%A Lee, Young
%A Mishra, Swapnil
%A Xie, Lexing
%D 2017
%J arXiv:1708.06401 cs, stat
%K - Computer Information Learning, Machine Networks Science Social Statistics and point processes
%T A Tutorial on Hawkes Processes for Events in Social Media
%U http://arxiv.org/abs/1708.06401
%X This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix
@article{rizoiu_tutorial_2017,
abstract = {This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event simulation and parameter estimation. We also describe a practical example drawn from social media data - we show how to model retweet cascades using a Hawkes self-exciting process. We presents a design of the memory kernel, and results on estimating parameters and predicting popularity. The code and sample event data are available as an online appendix},
added-at = {2020-01-31T19:19:18.000+0100},
author = {Rizoiu, Marian-Andrei and Lee, Young and Mishra, Swapnil and Xie, Lexing},
biburl = {https://www.bibsonomy.org/bibtex/286d79a5b0b08e5b9a5e3aba9180ada51/mannbachm},
file = {arXiv Fulltext PDF:C\:\\Users\\Jan\\Zotero\\storage\\B73CE36V\\Rizoiu et al. - 2017 - A Tutorial on Hawkes Processes for Events in Socia.pdf:application/pdf;arXiv.org Snapshot:C\:\\Users\\Jan\\Zotero\\storage\\HCXGBSM4\\1708.html:text/html},
interhash = {176a3b86f06615295ec6f7afbd14331a},
intrahash = {86d79a5b0b08e5b9a5e3aba9180ada51},
journal = {arXiv:1708.06401 [cs, stat]},
keywords = {- Computer Information Learning, Machine Networks Science Social Statistics and point processes},
month = oct,
note = {arXiv: 1708.06401},
timestamp = {2020-01-31T19:20:14.000+0100},
title = {A {Tutorial} on {Hawkes} {Processes} for {Events} in {Social} {Media}},
url = {http://arxiv.org/abs/1708.06401},
urldate = {2019-12-17},
year = 2017
}