网络中的社区-一种持续的方法
Communities in networks - a continuous approach
摘要：
提出了一个微分方程系统，用于识别加权网络中的社区。输入是对称连接矩阵$A_{ij}$。不需要关于社区数目的先验信息。为了验证动力学，我们准备了一组独立的、完全连接的集群。在这种情况下，矩阵$A$具有零和单位的块结构。噪声被引入为正随机数加到零并从单位中减去。动力学的任务是再现初始块结构。在该测试中，如果集群的数量大于四，则系统优于模块化算法。
英文摘要：
A system of differential equations is proposed designed as to identify communities in weighted networks. The input is a symmetric connectivity matrix $A_{ij}$. A priori information on the number of communities is not needed. To verify the dynamics, we prepared sets of separate, fully connected clusters. In this case, the matrix $A$ has a block structure of zeros and units. A noise is introduced as positive random numbers added to zeros and subtracted from units. The task of the dynamics is to reproduce the initial block structure. In this test, the system outperforms the modularity algorithm, if the number of clusters is larger than four.
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