Temporal patterns behind the strength of persistent ties
Abstract Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2017-12-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1140/epjds/s13688-017-0127-3 |
| _version_ | 1819079864222744576 |
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| author | Henry Navarro Giovanna Miritello Arturo Canales Esteban Moro |
| author_facet | Henry Navarro Giovanna Miritello Arturo Canales Esteban Moro |
| author_sort | Henry Navarro |
| collection | DOAJ |
| description | Abstract Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution. |
| first_indexed | 2024-12-21T19:35:45Z |
| format | Article |
| id | doaj.art-aed2aafa8d394ffa96ec18a8ca20b053 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-21T19:35:45Z |
| publishDate | 2017-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-aed2aafa8d394ffa96ec18a8ca20b0532022-12-21T18:52:36ZengSpringerOpenEPJ Data Science2193-11272017-12-016111910.1140/epjds/s13688-017-0127-3Temporal patterns behind the strength of persistent tiesHenry Navarro0Giovanna Miritello1Arturo Canales2Esteban Moro3Departamento de Matemáticas & GISC, Universidad Carlos III de MadridDepartamento de Matemáticas & GISC, Universidad Carlos III de MadridTelefónica I+DDepartamento de Matemáticas & GISC, Universidad Carlos III de MadridAbstract Social networks are made out of strong and weak ties having very different structural and dynamical properties. But what features of human interaction build a strong tie? Here we approach this question from a practical way by finding what are the properties of social interactions that make ties more persistent and thus stronger to maintain social interactions in the future. Using a large longitudinal mobile phone database we build a predictive model of tie persistence based on intensity, intimacy, structural and temporal patterns of social interaction. While our results confirm that structural (embeddedness) and intensity (number of calls) features are correlated with tie persistence, temporal features of communication events are better and more efficient predictors for tie persistence. Specifically, although communication within ties is always bursty we find that ties that are more bursty than the average are more likely to decay, signaling that tie strength is not only reflected in the intensity or topology of the network, but also on how individuals distribute time or attention across their relationships. We also found that stable relationships have and require a constant rhythm and if communication is halted for more than 8 times the previous communication frequency, most likely the tie will decay. Our results not only are important to understand the strength of social relationships but also to unveil the entanglement between the different temporal scales in networks, from microscopic tie burstiness and rhythm to macroscopic network evolution.http://link.springer.com/article/10.1140/epjds/s13688-017-0127-3social networkstie strengthtemporal patterns |
| spellingShingle | Henry Navarro Giovanna Miritello Arturo Canales Esteban Moro Temporal patterns behind the strength of persistent ties EPJ Data Science social networks tie strength temporal patterns |
| title | Temporal patterns behind the strength of persistent ties |
| title_full | Temporal patterns behind the strength of persistent ties |
| title_fullStr | Temporal patterns behind the strength of persistent ties |
| title_full_unstemmed | Temporal patterns behind the strength of persistent ties |
| title_short | Temporal patterns behind the strength of persistent ties |
| title_sort | temporal patterns behind the strength of persistent ties |
| topic | social networks tie strength temporal patterns |
| url | http://link.springer.com/article/10.1140/epjds/s13688-017-0127-3 |
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