Community Detection Algorithm Based on Network Feature Vector Space

Lidong Fu, Ruifeng Ma
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Abstract

Community detection plays an important role in the research of complex networks. It is very convenient to map the elements of the network into vector space to study the community structure of networks. Aiming at the problems that the number of communities is difficult to determine and the accuracy is not high in many community partition algorithms, a community detection algorithm based on network eigenvector space is proposed based on graph partition algorithm. The results show that the algorithm has better accuracy and the result of community partition is closer to the real community.
基于网络特征向量空间的社区检测算法
社区检测在复杂网络的研究中起着重要的作用。将网络元素映射到向量空间中来研究网络的群体结构是非常方便的。针对许多社区划分算法中社区数量难以确定、准确率不高的问题,在图划分算法的基础上,提出了一种基于网络特征向量空间的社区检测算法。结果表明,该算法具有更好的准确率,社区划分结果更接近真实社区。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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