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Disentangling degree and tie strength heterogeneity in egocentric social networks
Heydari, Sara Iñiguez, Gerardo Kertész, János Saramäki, Jari
Heydari, Sara
Iñiguez, Gerardo
Kertész, János
Saramäki, Jari
Title / Series / Name
EPJ Data Science
Publication Volume
13
Publication Issue
1
Pages
Editors
Keywords
Egocentric networks
Persistence
Personal networks
Social network analysis
Social signatures
Tie strength heterogeneity
Modeling and Simulation
Computer Science Applications
Computational Mathematics
Persistence
Personal networks
Social network analysis
Social signatures
Tie strength heterogeneity
Modeling and Simulation
Computer Science Applications
Computational Mathematics
Files
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Kertesz-Janos_2024-12.pdf
Adobe PDF, 1.93 MB
URI
https://hdl.handle.net/20.500.14018/29042
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).
Topic
Publisher
Place of Publication
Type
Journal article
Date
2024-12
Language
ISBN
Identifiers
10.1140/epjds/s13688-024-00513-x