Community structure detection based on node merging information entropy

TU Pei-wen; ZHOU Jin-he

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Community structure detection based on node merging information entropy

更新时间：2026-09-04

- Community structure detection based on node merging information entropy
- Vol. 37, Issue 7, Pages: 42-46(2020)
- 作者机构：
  
  北京信息科技大学机构信息与通信工程学院,北京,100101
- 作者简介：
- 基金信息：
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- Published：2020
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涂佩文, 周金和. 基于节点合并信息熵的社团结构检测[J]. 微电子学与计算机, 2020, 37(7): 42-46.

TU Pei-wen, ZHOU Jin-he. Community structure detection based on node merging information entropy[J]. 2020, 37(7): 42-46.
涂佩文, 周金和. 基于节点合并信息熵的社团结构检测[J]. 微电子学与计算机, 2020, 37(7): 42-46. DOI：

TU Pei-wen, ZHOU Jin-he. Community structure detection based on node merging information entropy[J]. 2020, 37(7): 42-46. DOI：

摘要

为解决目前社团划分存在的复杂度高及划分精度低等问题，从信息熵理论角度出发提出一种新的社团结构检测算法.针对节点划分概率系统事件发生的不确定性，采用相似性指标计算各节点合并所提供的信息量，结合全局重要度进一步构造节点合并信息熵模型，通过熵函数的值判断具有最小不确定度的合并方案，运用层次聚类的思想实现最终社团划分.通过对真实网络数据集的对比分析，验证了该算法的有效性，且划分社团具有较高的模块度.

Abstract

In order to solve the problems of high complexity and low division accuracy of the current community division

a new community structure detection algorithm is proposed from the perspective of information entropy theory. In view of the uncertainty of the occurrence of node division probability system events

the similarity index is used to calculate the information provided by node combination

and the further construction of node merge information entropy model is made by combining the global importance degree. The value of the entropy function is used to judge the merging scheme with the least uncertainty. Apply the idea of hierarchical clustering to realize the final community division. Through the comparison and analysis of real network datasets

the effectiveness of the algorithm is verified

and the divided community has a high degree of modularity.

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references

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