首页> 外文期刊>IEICE transactions on information and systems >Community Discovery on Multi-View Social Networks via Joint Regularized Nonnegative Matrix Triple Factorization
【24h】

Community Discovery on Multi-View Social Networks via Joint Regularized Nonnegative Matrix Triple Factorization

机译:通过联合正则化非负矩阵三因子分解的多视图社交网络上的社区发现

获取原文
获取外文期刊封面目录资料

摘要

In multi-view social networks field, a flexible Nonnegative Matrix Factorization (NMF) based framework is proposed which integrates multi-view relation data and feature data for community discovery. Benefit with a relaxed pairwise regularization and a novel orthogonal regularization, it outperforms the-state-of-art algorithms on five real-world datasets in terms of accuracy and NMI.
机译:在多视图社交网络领域,提出了一种基于灵活的非负矩阵分解(NMF)的框架,该框架将多视图关系数据和特征数据进行集成以进行社区发现。得益于轻松的成对正则化和新颖的正交正则化,它在准确性和NMI方面优于五个真实数据集上的最新算法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号