Improved community structure discovery algorithm based on penalised matrix decomposition for complex networks

Abstract

Complex networks are one of the main research fields in data mining. In this study, a penalised matrix decomposition-based community structure discovery algorithm (PMDCSDA) for complex networks is proposed. The complex network is firstly transformed into an adjacency matrix, which is then processed for dimension reduction via principal component analysis. Numerous clusters are produced on the basis of penalised matrix decomposition. To evaluate the performance of the proposed PMDCSDA, we compare it with several classical algorithms, such as K-means, CPM and GN, using three complex network datasets. Experimental results demonstrate that the proposed algorithm can achieve improved performance in precision, recall, F1 and Sep indicator.