在非定向随机网络上通过空洞方法分布中心性指标
Silvia Bartolucci1,2, Fabio Caccioli1,3,4, Francesco Caravelli5
1Department of Computer Science, University College London, London WC1E 6EA, United Kingdom.
概括
我们开发了一个快速的算法来计算随机网络中的卡茨中心性分布. 这种方法有效地解决复杂的分布方程,使得网络信息流更好地分析.
科学领域:
- 网络科学 网络科学
- 统计物理 统计物理
背景情况:
- 卡茨的中心性衡量了复杂网络中信息流的节点重要性.
- 在随机图中计算其全部概率分布在分析上具有挑战性,因为它具有全球性和矩阵反向定义.
研究的目的:
- 开发一种高效的分析方法,用于在随机图中计算卡茨中心的概率分布.
- 为快速计算和分布式分析利用信念传播算法.
主要方法:
- 利用快速的高斯式信念传播-空洞算法来递归计算单个实例的卡茨中心性.
- 采用人口动态算法来解决中心性概率分布的分布方程.
- 在Erdős-Rényi和Scale-Free网络组合中测试的方法在本地树状制度中.
主要成果:
- 证明了单个卡茨中心性的快速递归计算.
- 获得并有效地解决卡茨中心性概率的递归分布式方程.
- 通过对基准网络模型的模拟实现了良好的一致性.
结论:
- 开发的方法提供了一种有效的方法,以分析性地描述随机网络中的卡茨中心性分布.
- 该方法可以作为一个基准,用于识别在实证网络中具有异常中心性的节点.
- 该框架显示了扩展到其他中心性指标 (如PageRank) 的潜力.
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