Identification of intrinsic long-range degree correlations in complex networks
Identification of intrinsic long-range degree correlations in complex networks
复制标题
DOI：
10.1103/physreve.101.032308
复制
发表时间：
2019-04
期刊：
影响因子：
--
通讯作者：
Y. Fujiki;K. Yakubo
中科院分区：
文献类型：
--
作者：
Y. Fujiki;K. Yakubo
许多现实世界的网络都表现出被不止一步分隔的节点之间的度-度相关性。这种远程度相关性（LRDC）可以完全描述为一个联合和四个条件概率分布，相对于度的两个随机选择的节点和它们之间的最短路径距离。虽然LRDC是由相邻节点之间的最近邻度相关性（NNDC）引起的，但有些网络具有内在的LRDC，而NNDC不能产生。在这里，我们开发了一种方法来提取内在的LRDC在相关的网络，通过比较的概率分布为给定的网络与最近邻相关的随机网络。我们还证明了我们的方法的实用性，将其应用到几个现实世界的网络。
Many real-world networks exhibit degree-degree correlations between nodes separated by more than one step. Such long-range degree correlations (LRDCs) can be fully described by one joint and four conditional probability distributions with respect to degrees of two randomly chosen nodes and shortest path distance between them. While LRDCs are induced by nearest-neighbor degree correlations (NNDCs) between adjacent nodes, some networks possess intrinsic LRDCs which cannot be generated by NNDCs. Here we develop a method to extract intrinsic LRDC in a correlated network by comparing the probability distributions for the given network with those for nearest-neighbor correlated random networks. We also demonstrate the utility of our method by applying it to several real-world networks.