A weighted deep autoencoder (WDAE) based hybrid feature extraction model for the detection of communities in social networks-Reference-Cited by-同舟云学术

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A weighted deep autoencoder (WDAE) based hybrid feature extraction model for the detection of communities in social networks

Published:2023-08-12 Issue: Volume: Page:
ISSN:0975-6809
Container-title:International Journal of System Assurance Engineering and Management
language:en
Short-container-title:Int J Syst Assur Eng Manag

Author:

Suganthi R.,Prabha K.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Safety, Risk, Reliability and Quality

Link

https://link.springer.com/content/pdf/10.1007/s13198-023-02005-z.pdf

Reference29 articles.

1. Blekanov I, Bodrunova SS, Akhmetov A (2021) Detection of hidden communities in twitter discussions of varying volumes. Future Internet 13(11):295

2. Cai B, Wang Y, Zeng L, Hu Y, Li H (2020) Edge classification based on convolutional neural networks for community detection in complex network. Phys A 556:124826

3. Cao J, Jin D, Yang L, Dang J (2018) Incorporating network structure with node contents for community detection on large networks using deep learning. Neurocomputing 297:71–81

4. Chin JH, Ratnavelu K (2016) Detecting community structure by using a constrained label propagation algorithm. PLoS ONE 11(5):e0155320

5. Dhilber M, Bhavani SD (2020) Community detection in social networks using deep learning. In: International conference on distributed computing and internet technology (pp. 241–250). Springer, Cham

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exploring Link Prediction Techniques in Social Network Analysis for Community Detection;2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO);2024-06-14

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