【摘要】 针对复杂网络社区发现问题,为了获得更准确、可解释性的社区划分结果,提出融合先验信息的半监督非负矩阵分解算法,给出优化目标的求解方法.文中算法利用先验信息直接约束社区指示矩阵,构造优化目标函数,获得更有意义的社区划分结果.真实数据集上的实验表明该算法的有效性,减小先验信息的融入对利用非负矩阵分解进行节点重要性等属性分析工作带来的不利影响,并且适用于加权和非加权等不同的网络.更多还原

【Abstract】 To solve the problem of community detection in complex networks,a semi-supervised nonnegative matrix factorization( NMF) algorithm with prior information is proposed to obtain more accurate and better understanding results,and the detailed iteration algorithm is presented. In this algorithm,prior information is added to object function as additional constraints in community indicator matrix.Consequently,results are more meaningful. The experiments on real-world network datasets confirm the effectiveness of the proposed algorithm. It reduces the negative impact of the addition of prior information on node importance analysis with NMF,and it is suitable for weighted and un-weighted networks.更多还原