Modularity of online social networks acts as a reliable predictor of both whole-network and ego-network characteristics over time

Yizhang Zhao (), Wei Bai, Tianyu Qiao () and Weidong Wang ()
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Yizhang Zhao: Tsinghua University
Wei Bai: Tsinghua University
Tianyu Qiao: Peking University
Weidong Wang: Renmin University of China

Palgrave Communications, 2025, vol. 12, issue 1, 1-10

Abstract: Abstract In the digital age, social interactions have increasingly shifted online, necessitating a deeper understanding of the structure and dynamics of online social networks and their societal impacts. This study examines the stability of network modularity on relationship-based social media platforms and its predictive power for both whole-network structures and ego-network characteristics, using a nationally representative longitudinal dataset of Generation Z interactions on a popular social media platform in China. Our findings reveal that network modularity is a stable network attribute over time, which suggests that individuals tend to maintain existing contacts on relationship-based platforms and that the community structures of their online social networks are likely to persist. Notably, the initial level of network modularity significantly correlates with both whole-network and ego-network characteristics in subsequent periods, thus highlighting modularity’s power to predict long-term network characteristics. These insights contribute to social network theory by deepening our understanding of how the existence of smaller communities within network structures influences interpersonal interactions in digital communication, with broader implications for how social networks evolve within the landscape of social media.

Date: 2025
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DOI: 10.1057/s41599-025-05022-4

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