基于小波去噪的有向加权社团发现研究

目前大部分社团发现方法都是针对无向无权图,但实际的社会媒体中的社团内部个体交互过程可以抽象为一个有向加权图,并且权重中含有大量的噪声.为解决有向加权社团的划分问题,本文提出一种基于非负矩阵分解(Nonnegative matrix factorization,NMF)可去噪声的社团发现方法.该方法通过小波阈值去噪对社会网络数据进行去噪处理,结合有向加权的非负矩阵分解算法对去噪后的数据集进行社团发现,准确找出社团结构.在社会媒体的实验数据集和标准数据集上的实验结果表明,该算法针对带噪声的有向加权图社团发现问题具有良好划分性能,SNR为15时,在Lesmis数据集上的社团划分准确率达到96％,划分模块度值提高了29％.本文为解决带噪的有向加权的社会网络数据提供了切实有效的处理方法.

Weighted Directed Community Detection Method Based on Wavelet Denoising

Most community detection methods are aiming at solving undirected and unweighted datasets. However, datasets are often directed and weighted with noisein real world. In order to process noisy and directed weighted community detection, a method based on nonnegative matrix factorization (NMF) is proposed. In the algorithm, wavelet threshold denoising is used to denoise the social network datasets. And the community structure is abtained by community detection through NMF. Simulations show the proposed method is more effective,i.e. for Lesmis dataset when SNR is 15, the accuracy of dividing community is 96% and the modularity of the method is improved by 29%. The proposed method is more applicable than other community detection methods for directed weighted datasets with noise.