随机块体模型
邻接矩阵
块(置换群论)
计算机科学
节点(物理)
邻接表
块体模型
航程(航空)
数据挖掘
选择(遗传算法)
基质(化学分析)
算法
数学优化
数学
理论计算机科学
人工智能
组合数学
工程类
图形
复合材料
材料科学
聚类分析
结构工程
采矿工程
标识
DOI:10.1080/01621459.2016.1246365
摘要
The stochastic block model (SBM) and its variants have been a popular tool for analyzing large network data with community structures. In this article, we develop an efficient network cross-validation (NCV) approach to determine the number of communities, as well as to choose between the regular stochastic block model and the degree corrected block model (DCBM). The proposed NCV method is based on a block-wise node-pair splitting technique, combined with an integrated step of community recovery using sub-blocks of the adjacency matrix. We prove that the probability of under-selection vanishes as the number of nodes increases, under mild conditions satisfied by a wide range of popular community recovery algorithms. The solid performance of our method is also demonstrated in extensive simulations and two data examples. Supplementary materials for this article are available online.
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