Semi-supervised dynamic community detection based on non-negative matrix factorization - Zhen-chao CHANG; Hong-chang CHEN; Rui-yang HUANG; Hong-tao YU; Yang LIU (Tongxin xuebao, 2016)
How to effectively combine the network structures on different time points was the key and difficulty to affect the performance of detection algorithms. Based on this, a semi-supervised dynamic community algorithm SDCD based on non-negative matrix factorization, which effectively extracted the historical stability structure unit firstly, and then use it as a regularization item supervision of nonnegative matrix decomposition, to guide the network community detection on current moment. Experiments on the real network dat sets show that the method has a higher community detection quality compared with existing methods, which can accurately mine the relationship among different time, and explore network evolution and the law of development more adva geously.