基于非负矩阵分解的有向网络半监督社区检测

有向网络上的社区检测是网络科学领域一个重要的课题. 针对这一问题, 本文提出了一种基于非负矩阵分解的有向网络半监督社区检测算法, 首先利用先验信息重构邻接矩阵, 然后使用先验信息对节点的社区隶属度进行惩罚, 并通过行归一化消除节点度异质性的影响, 最后运用交替迭代更新给出了目标函数的求解方法. 在真实网络数据上的对比实验验证了算法的有效性, 相对于基于非负矩阵分解的现有方法, 本文方法能显著提高社区发现的准确性.

Semi-supervised Community Detection for Directed Network Based on Non-negative Matrix Factorization

Community detection for directed networks is an important topic in network science. Thus, this study proposes a semi-supervised community detection algorithm for directed networks based on non-negative matrix factorization (NMF). First, prior information is adopted to reconstruct the adjacency matrix and then penalize the community membership of nodes. Meanwhile, the influence of node degree heterogeneity is eliminated by row normalization, and finally, the objective function is solved using alternating iterative updates. Comparative experiments on real network datasets demonstrate the effectiveness of the proposed algorithm. Compared to existing NMF-based methods, this method can significantly improve community detection accuracy.