Understanding tie strength in social networks using a local “bow tie” framework
Understanding factors associated with tie strength in social networks is essential in a wide variety of settings. With the internet and cellular phones providing additional avenues of communication, measuring and inferring tie strength has become much more complex. We introduce the social bow tie fr...
Saved in:
Main Authors: | , , , |
---|---|
Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2018
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/86809 http://hdl.handle.net/10220/45327 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-86809 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-868092020-03-07T12:15:50Z Understanding tie strength in social networks using a local “bow tie” framework Mattie, Heather Engø-Monsen, Kenth Ling, Rich Onnela, Jukka-Pekka Wee Kim Wee School of Communication and Information Social Networks Bow Tie Framework Understanding factors associated with tie strength in social networks is essential in a wide variety of settings. With the internet and cellular phones providing additional avenues of communication, measuring and inferring tie strength has become much more complex. We introduce the social bow tie framework, which consists of a focal tie and all actors connected to either or both of the two focal nodes on either side of the focal tie. We also define several intuitive and interpretable metrics that quantify properties of the bow tie which enable us to investigate associations between the strength of the “central” tie and properties of the bow tie. We combine the bow tie framework with machine learning to investigate what aspects of the bow tie are most predictive of tie strength in two very different types of social networks, a collection of medium-sized social networks from 75 rural villages in India and a nationwide call network of European mobile phone users. Our results show that tie strength depends not only on the properties of shared friends, but also on non-shared friends, those observable to only one person in the tie, hence introducing a fundamental asymmetry to social interaction. Published version 2018-07-27T07:52:40Z 2019-12-06T16:29:22Z 2018-07-27T07:52:40Z 2019-12-06T16:29:22Z 2018 Journal Article Mattie, H., Engø-Monsen, K., Ling, R., & Onnela, J.-P. (2018). Understanding tie strength in social networks using a local “bow tie” framework. Scientific Reports, 8(1), 9349-. 2045-2322 https://hdl.handle.net/10356/86809 http://hdl.handle.net/10220/45327 10.1038/s41598-018-27290-8 en Scientific Reports © 2018 The Author(s) (Nature Publishing Group). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. 9 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
country |
Singapore |
collection |
DR-NTU |
language |
English |
topic |
Social Networks Bow Tie Framework |
spellingShingle |
Social Networks Bow Tie Framework Mattie, Heather Engø-Monsen, Kenth Ling, Rich Onnela, Jukka-Pekka Understanding tie strength in social networks using a local “bow tie” framework |
description |
Understanding factors associated with tie strength in social networks is essential in a wide variety of settings. With the internet and cellular phones providing additional avenues of communication, measuring and inferring tie strength has become much more complex. We introduce the social bow tie framework, which consists of a focal tie and all actors connected to either or both of the two focal nodes on either side of the focal tie. We also define several intuitive and interpretable metrics that quantify properties of the bow tie which enable us to investigate associations between the strength of the “central” tie and properties of the bow tie. We combine the bow tie framework with machine learning to investigate what aspects of the bow tie are most predictive of tie strength in two very different types of social networks, a collection of medium-sized social networks from 75 rural villages in India and a nationwide call network of European mobile phone users. Our results show that tie strength depends not only on the properties of shared friends, but also on non-shared friends, those observable to only one person in the tie, hence introducing a fundamental asymmetry to social interaction. |
author2 |
Wee Kim Wee School of Communication and Information |
author_facet |
Wee Kim Wee School of Communication and Information Mattie, Heather Engø-Monsen, Kenth Ling, Rich Onnela, Jukka-Pekka |
format |
Article |
author |
Mattie, Heather Engø-Monsen, Kenth Ling, Rich Onnela, Jukka-Pekka |
author_sort |
Mattie, Heather |
title |
Understanding tie strength in social networks using a local “bow tie” framework |
title_short |
Understanding tie strength in social networks using a local “bow tie” framework |
title_full |
Understanding tie strength in social networks using a local “bow tie” framework |
title_fullStr |
Understanding tie strength in social networks using a local “bow tie” framework |
title_full_unstemmed |
Understanding tie strength in social networks using a local “bow tie” framework |
title_sort |
understanding tie strength in social networks using a local “bow tie” framework |
publishDate |
2018 |
url |
https://hdl.handle.net/10356/86809 http://hdl.handle.net/10220/45327 |
_version_ |
1681034502825050112 |