在基于模块化和随机区块模型的双方用户内容网络中检测核心-外围结构
Lorenzo Federico1,2, Livia De Giovanni1,2, Alessandro Chessa2,3
1Department of Political Sciences, LUISS University, Rome, Italy.
Journal of applied statistics
|March 5, 2026
概括
这项研究分析了在Twitter上与意大利组织的受众互动,揭示了用户推特网络中的核心-外围结构. 大多数社区围绕中央用户或关键推文形成,与标准网络模型不同.
科学领域:
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
- 信息科学 信息科学 信息科学
背景情况:
- 了解观众在社交媒体上与组织的互动至关重要.
- 以前的网络模型往往无法捕捉现实世界的互动的复杂性.
研究的目的:
- 模拟和分析观众与意大利组织在Twitter上的互动模式.
- 调查网络结构,特别是核心-外围组织的存在.
- 检查这种结构如何与网络中的社区形成有关.
主要方法:
- 用户与推特的互动 (转发,回复) 的双边网络建模.
- 与零模型进行比较:双分配置模型,统一的双分图,"软"配置模型.
- 用于核心-外围分析的度度纠正静态块模型 (DC-SBM).
- 为社区结构分析进行双重模块化优化.
主要成果:
- 意大利的Twitter组织-受众网络表现出独特的结构,偏离了标准的零模型.
- 使用DC-SBM识别了一个独特的双边核心-外围组织.
- 社区结构主要由以核心用户或有影响力的推特为中心的星图组成.
结论:
- 该研究强调了在Twitter上与组织互动的特定网络动态.
- 确定了核心-外围结构和类似星级的社区,为在线参与模式提供了新的见解.
- 调查结果表明,与传统的网络模型有所不同,强调了核心参与者和内容的重要性.
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