论文标题
渗透实验重复实例的结果的异质性
Heterogeneity in Outcomes of Repeated Instances of Percolation Experiments
论文作者
论文摘要
我们使用消息传递方法评估属于巨型或渗透群集的异质性，依赖性概率的渗透实验的重复实例的结果的异质性，即，与系统大小相关的尺寸尺寸尺寸尺寸的相互连接节点的集合。我们将它们用于大型有限单实例，以及在热力学极限中的配置模型类中的合成网络。对于后者，我们将ERDOS-RENYI和比例免费网络都视为分别狭窄和广泛分布的网络的示例。对于现实世界网络，我们使用$ n = 62,568 $ nodes的Gnutella对等文件共享网络的无向版本。我们为不相关和相关的渗透过程的多个实例得出了理论。对于不相关的情况，我们还获得了Erdos-Renyi网络的大平均度限制的封闭形式近似。
We investigate the heterogeneity of outcomes of repeated instances of percolation experiments in complex networks using a message passing approach to evaluate heterogeneous, node dependent probabilities of belonging to the giant or percolating cluster, i.e. the set of mutually connected nodes whose size scales linearly with the size of the system. We evaluate these both for large finite single instances, and for synthetic networks in the configuration model class in the thermodynamic limit. For the latter, we consider both Erdos-Renyi and scale free networks as examples of networks with narrow and broad degree distributions respectively. For real-world networks we use an undirected version of a Gnutella peer-to-peer file-sharing network with $N=62,568$ nodes as an example. We derive the theory for multiple instances of both uncorrelated and correlated percolation processes. For the uncorrelated case, we also obtain a closed form approximation for the large mean degree limit of Erdos-Renyi networks.
