从组织间交换关系的分析中,从关系到事件
Federica Bianchi1, Alessandro Lomi1
1Università della Svizzera italiana, Lugano, Switzerland.
概括
关系事件模型 (REM) 通过使用事件序列和关系强度来增强网络分析,以揭示组织间交流中的短期和长期影响. 这些模型对于理解复杂的网络动态至关重要.
科学领域:
- 社交网络分析 社交网络分析
- 组织行为 组织行为
- 统计建模 统计建模
背景情况:
- 对于组织间网络的现有统计模型在分析顺序事件数据和关系强度方面存在局限性.
- 了解组织间依赖和交换的动态需要先进的分析工具.
研究的目的:
- 介绍和演示最近开发的关系事件模型 (REM),用于分析持续观察到的组织间交换关系.
- 突出REMs在有效利用顺序事件信息,关系强度和区分短期和长期网络效应方面的好处.
主要方法:
- 开发和应用一个关系事件模型 (REM),结合高效的采样算法和基于发送者的分层.
- 从高频率的银行间交易和医院患者共享网络中分析大规模的关系事件数据.
- 专注于直接和普遍的互惠,考虑复杂的依赖关系.
主要成果:
- 雷姆有效地利用顺序事件排序和关系强度,以获得更丰富的网络洞察力.
- 区分基于程度和强度以及短期和长期的网络效应对于理解组织间动态至关重要.
- 该模型在分析银行和医疗保健领域的大型,异构的参与者互动方面被证明是有价值的.
结论:
- 关系事件模型为组织间网络研究提供了扩展的分析能力.
- 这些发现强调了在网络分析中考虑时间和强度方面的重要性.
- 雷姆提供了一个强大的框架来检查组织内部和组织之间社交网络的演变动态.
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