Selection of significant community structure based on network partition-based cluster

被引：0

作者：

Jiao Q. ^{[1
]}

Jin Y. ^{[1
]}

机构：

[1] School of Computer and Information Engineering, Anyang Normal University, Anyang

来源：

Journal Europeen des Systemes Automatises | 2019年 / 52卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Complex network; Module structure; Multi-scale module detection; Significant partition;

D O I：

10.18280/jesa.520105

中图分类号：

学科分类号：

摘要：

Module (or community) structure detection, which has been successfully applied to many fields, is vital step for understand network dynamic and complex systems. Module structure not only is a crucial character of networks, but also is multi-scale. Therefore, many multi-scale module detection algorithms are proposed to resolve the problem. But a highly important issue for multi-scale methods is that of how to select crucial partitions among multi-scale network partitions so that these partitions can effectively help people to understand complex system. To solve the problem, we propose a novel partition-based hierarchical clustering to select significant network partitions. Experiments on selection of benchmark and real networks demonstrate that the new method for selecting significant partitions is very effectively. © 2019 Lavoisier. All rights reserved.

引用

页码：35 / 41

页数：6

共 31 条

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