论文标题
使用有序的随机图模型查找社区结构
Finding community structure using the ordered random graph model
论文作者
论文摘要
邻接矩阵的可视化使我们能够在正确对齐矩阵元素时捕获网络的宏观特征。社区结构是一个由几个密集连接的组件组成的网络，是一个特别重要的功能，当结构接近块 - 二角形形式时，可以通过邻接矩阵来识别结构。但是，用于矩阵的经典排序算法无法使矩阵元素对齐，从而可以看到社区结构。在这项研究中，我们根据有序随机图模型的最大样品估计提出了一种排序算法。我们表明，所提出的方法使我们能够比现有的订购算法更清楚地识别社区结构。
Visualization of the adjacency matrix enables us to capture macroscopic features of a network when the matrix elements are aligned properly. Community structure, a network consisting of several densely connected components, is a particularly important feature, and the structure can be identified through the adjacency matrix when it is close to a block-diagonal form. However, classical ordering algorithms for matrices fail to align matrix elements such that the community structure is visible. In this study, we propose an ordering algorithm based on the maximum-likelihood estimate of the ordered random graph model. We show that the proposed method allows us to more clearly identify community structures than the existing ordering algorithms.
