Journal
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
Volume 539, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.physa.2019.122904
Keywords
Community detection; Signed modularized regularization; Modularized convex nonnegative matrix factorization; Signed modularity density
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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.
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