Eindhoven University of Technology, Department of Mathematics and Computer Science, P.O. Box 513, 5600 MB Eindhoven, The Netherlands.
Sci Rep. 2016 Jul 21;6:29748. doi: 10.1038/srep29748.
Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both enforce as well as inhibit diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.
许多真实世界的网络都呈现出社区结构。我们研究了两种随机图模型，它们可以创建具有与给定网络相似社区结构的网络。一种模型保留了原始网络的精确社区结构，而另一种模型仅保留了社区集和顶点度数。这些模型表明，社区结构是网络上渗流过程（如信息扩散或病毒传播）行为的重要决定因素：社区结构既可以促进也可以抑制扩散过程。我们的模型还表明，重要的是中间尺度的社区集。社区的精确内部结构几乎不会影响渗流过程在网络中的行为。这种不敏感性可能是由于社区的相对密集度所致。
