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<p>Understanding how individuals learn and progress within complex social networks is crucial for various fields, from education to online gaming platforms. In this study, we investigate the impact of an individual’s topological position within a network of game interactions on their learning process. Leveraging a novel implementation of the state-of-the-art TrueSkill Through Time model, we accurately estimate the initial abilities of players in an online Go gaming platform. Utilizing dynamic graph analysis techniques, we analyze the centrality measures of individuals, including popularity, closeness, and intermediacy, to characterize their network positions. Our results reveal distinct learning patterns influenced by network centrality, particularly among individuals with intermediate initial abilities. Notably, we find significant differences in learning rates between players with low and high centrality, underscoring the role of network structure in shaping individual learning trajectories. These findings provide valuable insights into the interplay between social network dynamics and individual skill acquisition, with implications for optimizing learning environments and enhancing performance in complex social systems.</p>

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Network position and learning dynamics: unveiling the impact of social structure on skill acquisition in online gaming platforms

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Landfried Gustavo,
  • Cairo Gustavo,
  • Mocskos Esteban

摘要

Understanding how individuals learn and progress within complex social networks is crucial for various fields, from education to online gaming platforms. In this study, we investigate the impact of an individual’s topological position within a network of game interactions on their learning process. Leveraging a novel implementation of the state-of-the-art TrueSkill Through Time model, we accurately estimate the initial abilities of players in an online Go gaming platform. Utilizing dynamic graph analysis techniques, we analyze the centrality measures of individuals, including popularity, closeness, and intermediacy, to characterize their network positions. Our results reveal distinct learning patterns influenced by network centrality, particularly among individuals with intermediate initial abilities. Notably, we find significant differences in learning rates between players with low and high centrality, underscoring the role of network structure in shaping individual learning trajectories. These findings provide valuable insights into the interplay between social network dynamics and individual skill acquisition, with implications for optimizing learning environments and enhancing performance in complex social systems.