谱聚类在社团发现中的应用

Spectral Clustering for Community Detection

在分析谱聚类原理的基础上,研究了其在社团发现中的应用,提出了快速估计社团数量的新方法.该方法通过计算和分析Laplacian矩阵特征值的分布来估计社团的数量,利用K-means算法对Laplacian矩阵特征向量构造的向量空间进行聚类,实现社团的发现.该算法在真实社会网络和合成网络上做了测试,验证了在社团发现中的准确性和有效性.

In this paper spectral clustering was applied to detect the community in social network, and a new method was proposed to estimate the number of communities. According to this new method, the number of communities was estimated by calculating and analyzing the eigenvalues distribution of Laplacian matrix. K-means algorithm was used to clustering vector space which was constructed by eigenvectors of Laplacian matrix. The method was tested on a range of examples, including real-world and synthetic networks. Experimental results show that the method for community detection is accurate and effective.