A new approach for modeling mobility tables

Based on community detection methods

Bibliographic Data

ID6436701
AuthorsWangshu Sun (0000-0003-0691-8907, Dongbei University of Finance and Economics, corresponding author), Xu Sun (0000-0002-2057-2677, Dongbei University of Finance and Economics)
Year2025
Volume49
Issue2
Pages130-146
Publication date2025-02-23
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Mathematical Sociology (JOURNAL)
Journal identifiersISSN: 0022-250X • E-ISSN: 1545-5874
PublisherTaylor & Francis (PUBLISHER • GB)
DOI10.1080/0022250x.2025.2458292
OpenAlexW4407867472
LanguageEN
References cited24

Based on community detection methods in social networks, a new approach is presented for understanding and fitting social mobility processes. As a community detection algorithm, eigenspectrum decomposition is applied to identify optimal community structures in which most shares of the relations are within community ties. Through this approach, one may construct a log-linear model by adding community effects to an independence model until the model fits the given data, where the community effects are derived from the community detection analysis of the residuals. To illustrate, this approach is employed in mobility tables from the General Social Survey. Consequently, a set of parsimonious models that fit the observed mobility tables is presented, which offers novel and interesting interpretations of mobility patterns

Data mining · Complex Network Analysis Techniques · Computer Science · Human Mobility and Location-Based Analysis · Opportunistic and Delay-Tolerant Networks

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