Disentangling degree and tie strength heterogeneity in egocentric social networks
Abstract The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this featur...
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| Language: | English |
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SpringerOpen
2024-12-01
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| Series: | EPJ Data Science |
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| Online Access: | https://doi.org/10.1140/epjds/s13688-024-00513-x |
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| author | Sara Heydari Gerardo Iñiguez János Kertész Jari Saramäki |
| author_facet | Sara Heydari Gerardo Iñiguez János Kertész Jari Saramäki |
| author_sort | Sara Heydari |
| collection | DOAJ |
| description | Abstract The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear—one possibility is that people’s traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual’s personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages). |
| format | Article |
| id | doaj-art-3c6502bee2c943a28400f012c08543a7 |
| institution | OA Journals |
| issn | 2193-1127 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SpringerOpen |
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| series | EPJ Data Science |
| spelling | doaj-art-3c6502bee2c943a28400f012c08543a72025-08-20T02:31:41ZengSpringerOpenEPJ Data Science2193-11272024-12-0113111610.1140/epjds/s13688-024-00513-xDisentangling degree and tie strength heterogeneity in egocentric social networksSara Heydari0Gerardo Iñiguez1János Kertész2Jari Saramäki3Department of Computer Science, Aalto University School of ScienceFaculty of Information Technology and Communication Sciences, Tampere UniversityDepartment of Network and Data Science, Central European UniversityDepartment of Computer Science, Aalto University School of ScienceAbstract The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pattern, there is a substantial and persistent inter-individual variation that is also similarly distributed among channels. The reason for the observed universality is yet unclear—one possibility is that people’s traits determine their personal network features on any channel. To address this hypothesis, we need to compare an individual’s personal networks across channels, which is a non-trivial task: while we are interested in measuring the differences in tie strength heterogeneity, personal network size is also expected to vary a lot across channels. Therefore, for any measure that compares personal networks, one needs to understand the sensitivity with respect to network size. Here, we study different measures of personal network similarity and show that a recently introduced alter-preferentiality parameter and the Gini coefficient are equally suitable measures for tie strength heterogeneity, as they are fairly insensitive to differences in network size. With these measures, we show that the earlier observed individual-level persistence of personal network structure cannot be attributed to network size stability alone, but that the tie strength heterogeneity is persistent too. We also demonstrate the effectiveness of the two measures on multichannel data, where tie strength heterogeneity in personal networks is seen to moderately correlate for the same users across two communication channels (calls and text messages).https://doi.org/10.1140/epjds/s13688-024-00513-xSocial network analysisPersonal networksEgocentric networksTie strength heterogeneitySocial signaturesPersistence |
| spellingShingle | Sara Heydari Gerardo Iñiguez János Kertész Jari Saramäki Disentangling degree and tie strength heterogeneity in egocentric social networks EPJ Data Science Social network analysis Personal networks Egocentric networks Tie strength heterogeneity Social signatures Persistence |
| title | Disentangling degree and tie strength heterogeneity in egocentric social networks |
| title_full | Disentangling degree and tie strength heterogeneity in egocentric social networks |
| title_fullStr | Disentangling degree and tie strength heterogeneity in egocentric social networks |
| title_full_unstemmed | Disentangling degree and tie strength heterogeneity in egocentric social networks |
| title_short | Disentangling degree and tie strength heterogeneity in egocentric social networks |
| title_sort | disentangling degree and tie strength heterogeneity in egocentric social networks |
| topic | Social network analysis Personal networks Egocentric networks Tie strength heterogeneity Social signatures Persistence |
| url | https://doi.org/10.1140/epjds/s13688-024-00513-x |
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