Fitzwilliam College, University of Cambridge, Cambridge CB3 0DG, United Kingdom.
Mathematics Institute, University of Warwick, Coventry CV4 7AL, United Kingdom.
Proc Natl Acad Sci U S A. 2020 Nov 17;117(46):28582-28588. doi: 10.1073/pnas.2006465117. Epub 2020 Nov 2.
Network science and data analytics are used to quantify static and dynamic structures in George R. R. Martin's epic novels, , works noted for their scale and complexity. By tracking the network of character interactions as the story unfolds, it is found that structural properties remain approximately stable and comparable to real-world social networks. Furthermore, the degrees of the most connected characters reflect a cognitive limit on the number of concurrent social connections that humans tend to maintain. We also analyze the distribution of time intervals between significant deaths measured with respect to the in-story timeline. These are consistent with power-law distributions commonly found in interevent times for a range of nonviolent human activities in the real world. We propose that structural features in the narrative that are reflected in our actual social world help readers to follow and to relate to the story, despite its sprawling extent. It is also found that the distribution of intervals between significant deaths in chapters is different to that for the in-story timeline; it is geometric rather than power law. Geometric distributions are memoryless in that the time since the last death does not inform as to the time to the next. This provides measurable support for the widely held view that significant deaths in are unpredictable chapter by chapter.
网络科学和数据分析被用于量化乔治·R·R·马丁的史诗小说《冰与火之歌》中的静态和动态结构，这些作品以其规模和复杂性而闻名。通过跟踪故事展开过程中角色互动的网络，发现结构特性保持相对稳定且可与现实世界的社交网络相媲美。此外，最具连接性的角色的度数反映了人类在认知上倾向于维持的并发社交关系的数量有限。我们还分析了根据故事时间线测量的重大死亡事件之间时间间隔的分布。这些分布与现实世界中非暴力人类活动的各种事件间隔中常见的幂律分布一致。我们提出，反映在我们现实社会中的叙事结构特征有助于读者理解和关联故事，尽管故事篇幅庞大。此外，章节中重大死亡事件之间的间隔分布与故事时间线不同，呈几何分布而不是幂律分布。几何分布是无记忆的，因为上一次死亡的时间并不能提供下一次死亡的时间信息。这为广泛持有的观点提供了可衡量的支持，即《冰与火之歌》中的重大死亡事件在每一章都是不可预测的。
