Intensity and coherence of motifs in weighted complex networks -: art. no. 065103
Intensity and coherence of motifs in weighted complex networks -: art. no. 065103
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DOI：
10.1103/physreve.71.065103
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发表时间：
2005-06-01
影响因子：
2.4
通讯作者：
Kaski, K
中科院分区：
文献类型：
--
作者：
Onnela, JP;Saramäki, J;Kaski, K
未加权网络的局部结构可以用子图在网络中出现的次数来表征。聚类系数反映了三角形的局部结构，可以看作是这种方法的一个特例。在本文中，我们推广这种方法的加权网络。我们引入子图的“强度”作为其链接权重的几何平均值，“相干性”作为几何平均值与相应算术平均值的比值。利用这些度量，模体得分和聚类系数可以推广到加权网络。为了证明这些概念，我们将它们应用到金融和代谢网络，并发现，包括权重可能会大大修改从未加权特征的研究中获得的结论。
The local structure of unweighted networks can be characterized by the number of times a subgraph appears in the network. The clustering coefficient, reflecting the local configuration of triangles, can be seen as a special case of this approach. In this paper we generalize this method for weighted networks. We introduce subgraph "intensity" as the geometric mean of its link weights and "coherence" as the ratio of the geometric to the corresponding arithmetic mean. Using these measures, motif scores and clustering coefficient can be generalized to weighted networks. To demonstrate these concepts, we apply them to financial and metabolic networks and find that inclusion of weights may considerably modify the conclusions obtained from the study of unweighted characteristics.