What is the point of change?: Change point detection in relational event models

Mahdi Shafiee Kamalabad; Roger Leenders; Joris Mulder

doi:10.1016/j.socnet.2023.03.004

What is the point of change? Change point detection in relational event models

Mahdi Shafiee Kamalabad
, Roger Leenders
, Joris Mulder

Research output: Contribution to journal › Article › Scientific › peer-review

2 Citations (Scopus)

90 Downloads (Pure)

Abstract

This paper presents an extension to the relational event model with change points (REM-CP) to study abrupt changes to social interaction behavior in temporal networks. A change point detection algorithm is proposed for exploring when and which network effects abruptly change, and a confirmatory approach to test the presence of a change point at a given moment. The effectiveness of the methodology was assessed with numerical simulations and NASA’s Apollo 13 mission data. The latter revealed dynamic communication behavior and identified time zones where most change points occurred, including around the time of the famous quote “Houston, we’ve had a problem.”

Original language	English
Pages (from-to)	166-181
Journal	Social Networks
Volume	74
DOIs	https://doi.org/10.1016/j.socnet.2023.03.004
Publication status	Published - 2023

Keywords

Bayes factor
Change point detection
Communication
Critical incident analysis
Critical situation
Graceful extensibility
Relational event model
Resilience engineering
Social network analysis

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10.1016/j.socnet.2023.03.004Licence: CC BY

MTO Kamalabad what is the point of change SN 2023Final published version, 11.9 MBLicence: CC BY

Cite this

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title = "What is the point of change?: Change point detection in relational event models",

abstract = "This paper presents an extension to the relational event model with change points (REM-CP) to study abrupt changes to social interaction behavior in temporal networks. A change point detection algorithm is proposed for exploring when and which network effects abruptly change, and a confirmatory approach to test the presence of a change point at a given moment. The effectiveness of the methodology was assessed with numerical simulations and NASA{\textquoteright}s Apollo 13 mission data. The latter revealed dynamic communication behavior and identified time zones where most change points occurred, including around the time of the famous quote “Houston, we{\textquoteright}ve had a problem.”",

keywords = "Bayes factor, Change point detection, Communication, Critical incident analysis, Critical situation, Graceful extensibility, Relational event model, Resilience engineering, Social network analysis",

author = "\{Shafiee Kamalabad\}, Mahdi and Roger Leenders and Joris Mulder",

note = "This work was supported by an ERC Starting Grant (758791).",

year = "2023",

doi = "10.1016/j.socnet.2023.03.004",

language = "English",

volume = "74",

pages = "166--181",

journal = "Social Networks",

issn = "0378-8733",

publisher = "Elsevier B.V.",

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T1 - What is the point of change?

T2 - Change point detection in relational event models

AU - Shafiee Kamalabad, Mahdi

AU - Leenders, Roger

AU - Mulder, Joris

N1 - This work was supported by an ERC Starting Grant (758791).

PY - 2023

Y1 - 2023

N2 - This paper presents an extension to the relational event model with change points (REM-CP) to study abrupt changes to social interaction behavior in temporal networks. A change point detection algorithm is proposed for exploring when and which network effects abruptly change, and a confirmatory approach to test the presence of a change point at a given moment. The effectiveness of the methodology was assessed with numerical simulations and NASA’s Apollo 13 mission data. The latter revealed dynamic communication behavior and identified time zones where most change points occurred, including around the time of the famous quote “Houston, we’ve had a problem.”

AB - This paper presents an extension to the relational event model with change points (REM-CP) to study abrupt changes to social interaction behavior in temporal networks. A change point detection algorithm is proposed for exploring when and which network effects abruptly change, and a confirmatory approach to test the presence of a change point at a given moment. The effectiveness of the methodology was assessed with numerical simulations and NASA’s Apollo 13 mission data. The latter revealed dynamic communication behavior and identified time zones where most change points occurred, including around the time of the famous quote “Houston, we’ve had a problem.”

KW - Bayes factor

KW - Change point detection

KW - Communication

KW - Critical incident analysis

KW - Critical situation

KW - Graceful extensibility

KW - Relational event model

KW - Resilience engineering

KW - Social network analysis

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DO - 10.1016/j.socnet.2023.03.004

M3 - Article

SN - 0378-8733

VL - 74

SP - 166

EP - 181

JO - Social Networks

JF - Social Networks

ER -

What is the point of change? Change point detection in relational event models

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Keywords

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