Estimating tie strength in social networks using temporal communication data

Javier Urena Carrion, Jari Saramäki, Mikko Kivelä

Research output: Contribution to journalArticleScientificpeer-review

3 Citations (Scopus)
27 Downloads (Pure)

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.
Original languageEnglish
Article number37
Number of pages20
JournalEPJ Data Science
Volume9
Issue number1
DOIs
Publication statusPublished - 3 Dec 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • social network
  • Tie strength
  • call detail record
  • communication networks

Fingerprint

Dive into the research topics of 'Estimating tie strength in social networks using temporal communication data'. Together they form a unique fingerprint.

Cite this