Measuring Knowledge and Experience in Two Mode Temporal NetworksCitation formats

Standard

Measuring Knowledge and Experience in Two Mode Temporal Networks. / Everett, Martin; Broccatelli, Chiara; Borgatti, Steve; Koskinen, Johan.

In: Social Networks, Vol. 55, 10.2018, p. 63-73.

Research output: Contribution to journalArticlepeer-review

Harvard

Everett, M, Broccatelli, C, Borgatti, S & Koskinen, J 2018, 'Measuring Knowledge and Experience in Two Mode Temporal Networks', Social Networks, vol. 55, pp. 63-73. https://doi.org/10.1016/j.socnet.2018.05.003

APA

Everett, M., Broccatelli, C., Borgatti, S., & Koskinen, J. (2018). Measuring Knowledge and Experience in Two Mode Temporal Networks. Social Networks, 55, 63-73. https://doi.org/10.1016/j.socnet.2018.05.003

Vancouver

Everett M, Broccatelli C, Borgatti S, Koskinen J. Measuring Knowledge and Experience in Two Mode Temporal Networks. Social Networks. 2018 Oct;55:63-73. https://doi.org/10.1016/j.socnet.2018.05.003

Author

Everett, Martin ; Broccatelli, Chiara ; Borgatti, Steve ; Koskinen, Johan. / Measuring Knowledge and Experience in Two Mode Temporal Networks. In: Social Networks. 2018 ; Vol. 55. pp. 63-73.

Bibtex

@article{f019fc6e915f45bb91308b47debc3c20,
title = "Measuring Knowledge and Experience in Two Mode Temporal Networks",
abstract = "Two mode social network data consisting of actors attending events is a common type of social network data. For these kinds of data it is also common to have additional information about the timing or sequence of the events. We call data of this type two-mode temporal data. We explore the idea that actors attending events gain information from the event in two ways. Firstly the event itself may provide information or training; secondly, as co-attendees interact, they may pass on skills or information they have gleaned from other events. We propose a method of measuring these gains and demonstrate its usefulness using the classic Southern Women Data and a covert network dataset.",
keywords = "Two-mode networks, temporal networks, knowledge, experience, covert networks",
author = "Martin Everett and Chiara Broccatelli and Steve Borgatti and Johan Koskinen",
year = "2018",
month = oct,
doi = "10.1016/j.socnet.2018.05.003",
language = "English",
volume = "55",
pages = "63--73",
journal = "Social Networks",
issn = "0378-8733",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Measuring Knowledge and Experience in Two Mode Temporal Networks

AU - Everett, Martin

AU - Broccatelli, Chiara

AU - Borgatti, Steve

AU - Koskinen, Johan

PY - 2018/10

Y1 - 2018/10

N2 - Two mode social network data consisting of actors attending events is a common type of social network data. For these kinds of data it is also common to have additional information about the timing or sequence of the events. We call data of this type two-mode temporal data. We explore the idea that actors attending events gain information from the event in two ways. Firstly the event itself may provide information or training; secondly, as co-attendees interact, they may pass on skills or information they have gleaned from other events. We propose a method of measuring these gains and demonstrate its usefulness using the classic Southern Women Data and a covert network dataset.

AB - Two mode social network data consisting of actors attending events is a common type of social network data. For these kinds of data it is also common to have additional information about the timing or sequence of the events. We call data of this type two-mode temporal data. We explore the idea that actors attending events gain information from the event in two ways. Firstly the event itself may provide information or training; secondly, as co-attendees interact, they may pass on skills or information they have gleaned from other events. We propose a method of measuring these gains and demonstrate its usefulness using the classic Southern Women Data and a covert network dataset.

KW - Two-mode networks, temporal networks, knowledge, experience, covert networks

U2 - 10.1016/j.socnet.2018.05.003

DO - 10.1016/j.socnet.2018.05.003

M3 - Article

VL - 55

SP - 63

EP - 73

JO - Social Networks

JF - Social Networks

SN - 0378-8733

ER -