Research on community detection algorithm based on content similarity

ZHONG Lei

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Research on community detection algorithm based on content similarity

更新时间：2026-09-04

- Research on community detection algorithm based on content similarity
- Vol. 38, Issue 4, Pages: 69-73(2021)
- 作者机构：
  
  1. 新疆财经大学机构信息管理学院,新疆,乌鲁木齐,830012
  2. 新疆财经大学统计与数据科学学院,新疆,乌鲁木齐,830012
- 作者简介：
- 基金信息：
- DOI：
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- Published：2021
- 稿件说明：
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钟磊. 基于内容相似度的社区发现算法研究[J]. 微电子学与计算机, 2021, 38(4): 69-73.

ZHONG Lei. Research on community detection algorithm based on content similarity[J]. 2021, 38(4): 69-73.
钟磊. 基于内容相似度的社区发现算法研究[J]. 微电子学与计算机, 2021, 38(4): 69-73. DOI：

ZHONG Lei. Research on community detection algorithm based on content similarity[J]. 2021, 38(4): 69-73. DOI：

摘要

传统的社区发现算法中网络节点相似度多以空间距离度量，这种度量往往不容易理解，或者只能从距离的角度予以解释.本文提出一种基于文本内容相似度的谱聚类方法，它以网络社区用户的文本信息的相似性来度量网络节点的属性相似度，考虑网络结构的同时使节点的属性联系更有意义.在此基础上使用谱聚类的思想进行社区划分.本文以实际数据进行实验发现，发现划分结果的模块度和节点平均相似度较高，聚类效果良好.

Abstract

In traditional community detection algorithms

the similarity of network nodes is usually measured by spatial distance

which is not easy to understand or can only be explained from the perspective of distance. This paper proposes a spectral clustering method based on the similarity of text content

which measures the attribute similarity of network nodes by the similarity of text information of users in the network community

and makes the attribute connection of nodes more meaningful when considering the network structure. On this basis

the idea of spectral clustering is used to divide the community. The experimental results show that the modular degree and the average similarity of nodes are high

and the clustering effect is good.

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references

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