论文标题
通过有偏见的随机步行重新布线的社区结构的出现
Emergence of community structures through biased random walks rewiring
论文作者
论文摘要
在各种复杂的现实世界网络中已经确定了社区结构，例如通信，信息，互联网和股东网络。社区大小分布的缩放表明网络拓扑结构的异质性。当前的网络生成或增长模型可以重现某些属性，包括学位分布，大型聚类系数和社区。但是，社区规模的规模行为缺乏调查，尤其是从当地互动的角度来看。基于以下假设：异质节点的行为不同并导致网络的不同拓扑位置，我们提出了一个在有导网络中设计的随机步行模型，以解释观察到的网络中的特征。该模型强调，两个不同的动态可以模仿局部相互作用，而在重现真实复杂网络的特征时，隐藏的层是必不可少的。该模型可以解释的关键功能包括社区尺寸分布，程度分布，渗透属性，平均路径长度的分布以及上述属性对数据中节点标签的依赖性。
Community structures have been identified in various complex real-world networks, for example, communication, information, internet and shareholder networks. The scaling of community size distribution indicates the heterogeneity in the topological structures of the network. The current network generating or growing models can reproduce some properties, including degree distributions, large clustering coefficients and communities. However, the scaling behaviour of the community size lacks investigation, especially from the perspectives of local interactions. Based on the assumption that heterogeneous nodes behave differently and result in different topological positions of the networks, we propose a model of designed random walks in directed networks to explain the features in the observed networks. The model highlights that two different dynamics can mimic the local interactions, and a hidden layer is essential when reproducing the characteristics of real complex networks. The key features the model can explain include community size distribution, degree distribution, percolation properties, distribution of average path length and dependence of the above properties on the labels of nodes in the data.
