期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 539, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.physa.2019.122904
关键词
Community detection; Signed modularized regularization; Modularized convex nonnegative matrix factorization; Signed modularity density
NMF-based models in unsigned networks, the links of which are positive links only, have been applied in many aspects, such as community detection, link prediction, etc. However, NMF has been under-explored for community discovery in signed networks due to its constraint of non-negativity. Also, there are few related studies which could find out accurate partitions on both signed and unsigned networks due to their difference of community structure. In this paper, we propose a novel modularized convex nonnegative matrix factorization model which combines signed modularized information with convex NMF model, improving the accuracy of community detection in signed and unsigned networks. As for model selection, we extend the modularity density to signed networks and employ the signed modularity density to determine the number of communities automatically. Finally, the effectiveness of our model is verified on both synthetic and real-world networks. (C) 2019 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据