Graph Regularized Nonnegative Matrix Factorization for Community Detection in Attributed Networks
Description
Community detection has become an important research topic in machine learning due to the proliferation of network data. However, most existing methods have been developed based on only exploiting the topology structures of the network, which can result in missing the advantage of using the nodes' attribute information. As a result, it is expected that much valuable information that could be used to improve the quality of discovered communities will be ignored. To solve this limitation, we propose a novel Augment Graph Regularization Nonnegative Matrix Factorization for Attributed Networks (AGNMF-AN) method, which is simple yet effective. Firstly, Augment Attributed Graph (AAG) is applied to combine both the topological structure and attributed nodes of the network. Secondly, we introduced an effective framework to update the affinity matrix, in which the affinity matrix's weight in each iteration is modified adaptively instead of using a fixed affinity matrix in the classical graph regularization-based nonnegative matrix factorization methods. Thirdly, the l2,1 -norm is utilized to reduce the effect of random noise and outliers in the quality of structure community. Experimental results show that our method performs unexpectedly well in comparison to existing state-of-the-art methods in attributed networks.
Impact and interest:
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ID Code: | 244682 | ||||
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Item Type: | Contribution to Journal (Journal Article) | ||||
Refereed: | Yes | ||||
ORCID iD: |
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Measurements or Duration: | 14 pages | ||||
Keywords: | attributed networks, community detection, Complex network, nonnegative matrix factorization | ||||
DOI: | 10.1109/TNSE.2022.3210233 | ||||
ISSN: | 2327-4697 | ||||
Pure ID: | 150864000 | ||||
Divisions: | Current > QUT Faculties and Divisions > Faculty of Science Current > Schools > School of Computer Science |
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Copyright Owner: | 2022 IEEE | ||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||
Deposited On: | 24 Nov 2023 02:39 | ||||
Last Modified: | 03 Aug 2024 21:02 |
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