稠密加权网络社区：以MicroRNA协同目标网络为例
Communities of dense weighted networks: MicroRNA co-target network as an example
摘要：
复杂网络本质上是模块化的。当网络连接密集时，解析小模块特别困难;链路权重的广泛变化会带来额外的复杂性。在这篇文章中，我们提出了一个算法来检测社区结构的密集连接的加权网络。首先，通过擦除具有小于截止$q.$的权重的链接来计算网络的模块化。然后，取在模块化最大的$q=q_c,$处获得的所有不相交分量，并使用加权网络的Newman Girvan算法单独模块化分量。以人类microRNA（miRNA）共靶网络为例，证明了该算法可以揭示已知与生物学背景相关的miRNA模块。
英文摘要：
Complex networks are intrinsically modular. Resolving small modules is particularly difficult when the network is densely connected; wide variation of link weights invites additional complexities. In this article we present an algorithm to detect community structure in densely connected weighted networks. First, modularity of the network is calculated by erasing the links having weights smaller than a cutoff $q.$ Then one takes all the disjoint components obtained at $q=q_c,$ where the modularity is maximum, and modularize the components individually using Newman Girvan's algorithm for weighted networks. We show, taking microRNA (miRNA) co-target network of Homo sapiens as an example, that this algorithm could reveal miRNA modules which are known to be relevant in biological context.
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