基于非负矩阵分解的半监督动态社团检测

如何有效融合不同时刻的网络结构信息，是影响复杂网络中动态社团检测算法检测性能的关键和难点。基于此，提出了一种基于非负矩阵分解的半监督动态社团检测方法SDCD-NMF，该方法首先有效提取了历史时刻所包含的稳定结构单元，然后将其作为正则化监督项，指导当前时刻的网络社团检测。在真实网络数据集上的实验表明，所提方法与已有方法相比具备更高的社团划分质量，更有利于探索网络的演变与发展规律。

Semi-supervised dynamic community detection based on non-negative matrix factorization

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 data 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 advantageously.