Disentangling degree and tie strength heterogeneity in egocentric social networks

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).

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Publication Details

Journal
EPJ Data Science
Published
2024-12-18
DOI
https://doi.org/10.1140/epjds/s13688-024-00513-x
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.28

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article

Disentangling degree and tie strength heterogeneity in egocentric social networks

János Kertész, Sara Heydari, Jari Saramäki, Gerardo Íñiguez
1 citations
EPJ Data Science
Complex Network Analysis Techniques
0.28
article

Disentangling degree and tie strength heterogeneity in egocentric social networks

János Kertész, Sara Heydari, Jari Saramäki, Gerardo Íñiguez
article en
1 citations

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).

EPJ Data ScienceVol. 13(1)
Tampere University (FI), Universidad Autónoma de la Ciudad de México (MX), Central European University (AT), Complexity Science Hub (AT), Universidad Nacional Autónoma de México (MX), Aalto University (FI)
Air Force Office of Scientific Research, CHIST-ERA
Reduced inequalities
Openalex Percentile: Top 42%
Complex Network Analysis Techniques
0.28
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