在网络上传播的普遍限制
1Department of Applied Mathematics, School of Mathematical Sciences, Tel Aviv University, Tel Aviv 6997801, Israel.
Chaos (Woodbury, N.Y.)
|May 8, 2024
概括
社交网络上的创新传播受到同行影响的影响. 这项研究为采用率提供了严格的下限和上限,即使网络结构是未知的,使用Bass模型对特定网络类型.
科学领域:
- 社交网络分析 社交网络分析
- 创新的传播理论
- 随机过程 随机过程
背景情况:
- 创新传播依赖于社交网络和同行效应 (口碑).
- 网络结构对随时间推移的整体采用速度产生重大影响.
- 在现实世界的创新传播场景中,网络结构通常是未知的.
研究的目的:
- 估计未知的社交网络上的创新随时间推移的总体采用水平.
- 建立创新传播率的理论界限.
- 分析网络结构对采用概率的影响.
主要方法:
- 使用Bass模型,这是采用新产品的标准模型.
- 在两个特定的网络结构上分析扩散动态:一个均的双节点网络和一个均的无限完整网络.
- 导出预期采用水平和个人采用概率的明确下限和上限.
主要成果:
- 最低和最大的采用水平分别在同质的双节点和无限完整网络上实现.
- 对于任何网络结构来说,对预期采用水平的明确,严格的下限和上限都得到了推导.
- 这些边界准确地预测了个人的采用概率.
- 边界之间的差距随着内部与外部影响率的增加而扩大.
结论:
- 导出的边界提供了一个可靠的方法来估计在未知网络上的创新扩散.
- 这些发现适用于理解口碑营销和技术采用.
- 即使没有完整的信息,网络结构在传播中的作用也可以受到限制.
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