Estimating tie strength in social networks using temporal communication data
Abstract Even though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2020-12-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-020-00256-5 |
| _version_ | 1818844675348365312 |
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| author | Javier Ureña-Carrion Jari Saramäki Mikko Kivelä |
| author_facet | Javier Ureña-Carrion Jari Saramäki Mikko Kivelä |
| author_sort | Javier Ureña-Carrion |
| collection | DOAJ |
| description | Abstract Even though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This, however, disregards many features that can be extracted from the rich data sets used for social network reconstruction. Here, we focus on contact data with temporal information. We systematically study how features of the contact time series are related to topological features usually associated with tie strength. We focus on a large mobile-phone dataset and measure a number of properties of the contact time series for each tie and use these to predict the so-called neighbourhood overlap, a feature related to strong ties in the sociological literature. We observe a strong relationship between temporal features and the neighbourhood overlap, with many features outperforming simple contact counts. Features that stand out include the number of days with calls, number of bursty cascades, typical times of contacts, and temporal stability. These are also seen to correlate with the overlap in diverse smaller communication datasets studied for reference. Taken together, our results suggest that such temporal features could be useful for inferring social network structure from communication data. |
| first_indexed | 2024-12-19T05:17:32Z |
| format | Article |
| id | doaj.art-e441bf55caf642a7b013dcee750260c6 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-12-19T05:17:32Z |
| publishDate | 2020-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-e441bf55caf642a7b013dcee750260c62022-12-21T20:34:37ZengSpringerOpenEPJ Data Science2193-11272020-12-019112010.1140/epjds/s13688-020-00256-5Estimating tie strength in social networks using temporal communication dataJavier Ureña-Carrion0Jari Saramäki1Mikko Kivelä2School of Science, Aalto UniversitySchool of Science, Aalto UniversitySchool of Science, Aalto UniversityAbstract Even though the concept of tie strength is central in social network analysis, it is difficult to quantify how strong social ties are. One typical way of estimating tie strength in data-driven studies has been to simply count the total number or duration of contacts between two people. This, however, disregards many features that can be extracted from the rich data sets used for social network reconstruction. Here, we focus on contact data with temporal information. We systematically study how features of the contact time series are related to topological features usually associated with tie strength. We focus on a large mobile-phone dataset and measure a number of properties of the contact time series for each tie and use these to predict the so-called neighbourhood overlap, a feature related to strong ties in the sociological literature. We observe a strong relationship between temporal features and the neighbourhood overlap, with many features outperforming simple contact counts. Features that stand out include the number of days with calls, number of bursty cascades, typical times of contacts, and temporal stability. These are also seen to correlate with the overlap in diverse smaller communication datasets studied for reference. Taken together, our results suggest that such temporal features could be useful for inferring social network structure from communication data.https://doi.org/10.1140/epjds/s13688-020-00256-5Social networksTie StrengthCall Detail RecordsCommunication networks |
| spellingShingle | Javier Ureña-Carrion Jari Saramäki Mikko Kivelä Estimating tie strength in social networks using temporal communication data EPJ Data Science Social networks Tie Strength Call Detail Records Communication networks |
| title | Estimating tie strength in social networks using temporal communication data |
| title_full | Estimating tie strength in social networks using temporal communication data |
| title_fullStr | Estimating tie strength in social networks using temporal communication data |
| title_full_unstemmed | Estimating tie strength in social networks using temporal communication data |
| title_short | Estimating tie strength in social networks using temporal communication data |
| title_sort | estimating tie strength in social networks using temporal communication data |
| topic | Social networks Tie Strength Call Detail Records Communication networks |
| url | https://doi.org/10.1140/epjds/s13688-020-00256-5 |
| work_keys_str_mv | AT javierurenacarrion estimatingtiestrengthinsocialnetworksusingtemporalcommunicationdata AT jarisaramaki estimatingtiestrengthinsocialnetworksusingtemporalcommunicationdata AT mikkokivela estimatingtiestrengthinsocialnetworksusingtemporalcommunicationdata |