Investigating and modeling the dynamics of long ties
<jats:title>Abstract</jats:title><jats:p>Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2022
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| Online Access: | https://hdl.handle.net/1721.1/146597 |
| _version_ | 1811088753354080256 |
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| author | Lyu, Ding Yuan, Yuan Wang, Lin Wang, Xiaofan Pentland, Alex |
| author2 | MIT Connection Science (Research institute) |
| author_facet | MIT Connection Science (Research institute) Lyu, Ding Yuan, Yuan Wang, Lin Wang, Xiaofan Pentland, Alex |
| author_sort | Lyu, Ding |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties might dissolve quickly or eventually become redundant, thus putting into question the long-term value of long ties. Our empirical analysis of real-world dynamic networks shows that contrary to such reasoning, long ties are more likely to persist than other social ties, and that many of them constantly function as social bridges without being embedded in local networks. Using a cost-benefit analysis model combined with machine learning, we show that long ties are highly beneficial, which instinctively motivates people to expend extra effort to maintain them. This partly explains why long ties are more persistent than what has been suggested by many existing theories and models. Overall, our study suggests the need for social interventions that can promote the formation of long ties, such as mixing people with diverse backgrounds.</jats:p> |
| first_indexed | 2024-09-23T14:06:57Z |
| format | Article |
| id | mit-1721.1/146597 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:06:57Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1465972023-02-16T16:36:36Z Investigating and modeling the dynamics of long ties Lyu, Ding Yuan, Yuan Wang, Lin Wang, Xiaofan Pentland, Alex MIT Connection Science (Research institute) Massachusetts Institute of Technology. Media Laboratory <jats:title>Abstract</jats:title><jats:p>Long ties, the social ties that bridge different communities, are widely believed to play crucial roles in spreading novel information in social networks. However, some existing network theories and prediction models indicate that long ties might dissolve quickly or eventually become redundant, thus putting into question the long-term value of long ties. Our empirical analysis of real-world dynamic networks shows that contrary to such reasoning, long ties are more likely to persist than other social ties, and that many of them constantly function as social bridges without being embedded in local networks. Using a cost-benefit analysis model combined with machine learning, we show that long ties are highly beneficial, which instinctively motivates people to expend extra effort to maintain them. This partly explains why long ties are more persistent than what has been suggested by many existing theories and models. Overall, our study suggests the need for social interventions that can promote the formation of long ties, such as mixing people with diverse backgrounds.</jats:p> 2022-11-22T19:08:33Z 2022-11-22T19:08:33Z 2022 2022-11-22T19:03:30Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/146597 Lyu, Ding, Yuan, Yuan, Wang, Lin, Wang, Xiaofan and Pentland, Alex. 2022. "Investigating and modeling the dynamics of long ties." Communications Physics, 5 (1). en 10.1038/S42005-022-00863-W Communications Physics Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Lyu, Ding Yuan, Yuan Wang, Lin Wang, Xiaofan Pentland, Alex Investigating and modeling the dynamics of long ties |
| title | Investigating and modeling the dynamics of long ties |
| title_full | Investigating and modeling the dynamics of long ties |
| title_fullStr | Investigating and modeling the dynamics of long ties |
| title_full_unstemmed | Investigating and modeling the dynamics of long ties |
| title_short | Investigating and modeling the dynamics of long ties |
| title_sort | investigating and modeling the dynamics of long ties |
| url | https://hdl.handle.net/1721.1/146597 |
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