netseg: a Python Package for Measuring Structural Polarization and Segregation in Social Networks
netseg是一个Python包,用于量化社交网络中的结构极化和隔离现象,通过实现并优化多种网络指数来解决现有代码不公开、未经测试的问题。
netseg是一个Python包,用于量化社交网络中的结构极化和隔离现象,通过实现并优化多种网络指数来解决现有代码不公开、未经测试的问题。
本文解决了传统网络无法充分表示多节点、时间性及方向性交互的问题,通过引入超图和有向超图的时间模体,并开发了高效算法进行挖掘。
研究通过引入几何模型解释了复杂系统中不同层级交互为何呈现嵌套结构,并揭示了潜在几何作为组织原则的作用。
研究探讨了社交机器人在2020年黑人命也是命抗议活动期间对人类网络凝聚力的影响,通过分析转推网络的变化发现机器人暴露导致了后续支持者间凝聚力下降。
This study investigates the systematic overestimation of racial minority population proportions by the U.S. public across four nested geographic scales—neighborhood, city, state, and nation—and examines its underlying mechanisms. Drawing on a nationally representative survey integrated with multiscale spatial analysis, statistical modeling, and measures of media consumption, the research reveals that overestimation intensifies with increasing geographic scale. Racial minorities tend to overestimate the size of their own groups, while among White respondents, local-level overestimation is primarily driven by actual social contact, whereas national-level misperceptions are shaped by perceived news coverage. The study innovatively demonstrates the scale dependence and group heterogeneity of cognitive bias and provides the first evidence that news consumption attenuates overestimation, whereas social media use significantly exacerbates it.
netseg是一个Python包,用于量化社交网络中的结构极化和隔离现象,通过实现并优化多种网络指数来解决现有代码不公开、未经测试的问题。
本文解决了传统网络无法充分表示多节点、时间性及方向性交互的问题,通过引入超图和有向超图的时间模体,并开发了高效算法进行挖掘。
研究通过引入几何模型解释了复杂系统中不同层级交互为何呈现嵌套结构,并揭示了潜在几何作为组织原则的作用。
研究探讨了社交机器人在2020年黑人命也是命抗议活动期间对人类网络凝聚力的影响,通过分析转推网络的变化发现机器人暴露导致了后续支持者间凝聚力下降。
This study investigates the systematic overestimation of racial minority population proportions by the U.S. public across four nested geographic scales—neighborhood, city, state, and nation—and examines its underlying mechanisms. Drawing on a nationally representative survey integrated with multiscale spatial analysis, statistical modeling, and measures of media consumption, the research reveals that overestimation intensifies with increasing geographic scale. Racial minorities tend to overestimate the size of their own groups, while among White respondents, local-level overestimation is primarily driven by actual social contact, whereas national-level misperceptions are shaped by perceived news coverage. The study innovatively demonstrates the scale dependence and group heterogeneity of cognitive bias and provides the first evidence that news consumption attenuates overestimation, whereas social media use significantly exacerbates it.