动态加权网络中的演化社区发现算法研究

在动态网络中发现社区结构是一个非常复杂而有意义的过程，可以更好地观察和分析网络的演化情况。针对动态加权网络中的社区发现问题，提出了一种结合历史网络社区结构的算法，叫做动态加权网络中的演化社区发现算法（ECDA）。该算法分为两步：结合历史社区和网络结构信息，计算当前时间跳的输入矩阵；然后通过该输入矩阵计算得到结合历史时间跳信息的社区划分结果。该算法有以下优点：可以自动发现动态加权网络中每个时间跳的社区结构；对网络结构的变化和社区结构的变化具有较高的敏锐性。在人工数据集和真实数据集中进行了实验，实验结果证明该算法可以有效地发现动态加权网络中的社区结构，与其他算法相比具有较好的竞争力。

Research on evolutionary community discovery algorithm in dynamic weighted networks

In dynamic networks, detecting community structure is a very complex and meaningful process, which can better observe and analyze the evolution of the networks. For the community detection problem in dynamic weighted networks, this paper proposed an algorithm combining the community structure of the historical networks, called the evolutionary community discovery algorithm (ECDA) in dynamic weighted networks. The algorithm is divided into two steps: calculate the input matrix of the current timestep by combining the information of historical communities and network structure; and then calculate the result of community detection combining the historical timestep information through the input matrix. The algorithm has the following advantages: it can automatically discover the community structure of each timestep in the dynamic weighted network; the algorithm has a high sensitivity to the changes of network structure and the changes of community structure. And the experimental results show that the proposed algorithm can effectively detect the community structure in dynamic weighted networks, and it is quite competitive with other algorithms.