Abstract
Community structures are everywhere, from simple networks to real-world complex networks. Community structure is an important feature in complex networks, and community discovery has important application value for the study of social network structure. When dealing with high-dimensional matrices using classical clustering algorithms, the resulting communities are often inaccurate. In this paper, a community discovery algorithm based on an improved deep sparse autoencoder is proposed, which attempts to apply to the community discovery problem through two different network similarity representations. This can make up for the deficiency that a single network similarity matrix cannot fully describe the similarity relationship between nodes. These similarity representations can fully describe and consider local information between nodes in the network topology. Then, a weight-bound deep sparse autoencoder is constructed based on an unsupervised deep learning method to improve the efficiency of feature extraction. Finally, feature extraction is performed on the similarity matrix to obtain a low-dimensional feature matrix, and the k-means clustering method is used to cluster the low-dimensional feature matrix to obtain reliable clustering results. In various extensive experiments conducted on multiple real networks, the proposed method is more accurate than other community discovery algorithms using a single similarity matrix clustering algorithm, and the efficiency of the community discovery algorithm is much more improved.
This work was supported in part by National Key R &D Program of China (No. 2019YFB1707000).
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Acknowledgment
This work was supported in part by National Key R &D Program of China (No. 2019YFB1707000).
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Chen, D., Jiang, X., Chen, J., Wei, X. (2023). Community Discovery Algorithm Based on Improved Deep Sparse Autoencoder. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_50
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