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Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
Detecting a community in a network is a matter of discerning the distinct features and connections of a group of members that are different from those in other communities. The ability to do this is of great significance in network analysis. However, beyond the classic spectral clustering and statistical inference methods, there have been significant developments with deep learning techniques for community detection in recent years-particularly when it comes to handling high-dimensional network data. Hence, a comprehensive review of the latest progress in community detection through deep learning is timely. To frame the survey, we have devised a new taxonomy covering different state-of-the-art methods, including deep learning models based on deep neural networks (DNNs), deep nonnegative matrix factorization, and deep sparse filtering. The main category, i.e., DNNs, is further divided into convolutional networks, graph attention networks, generative adversarial networks, and autoencoders. The popular benchmark datasets, evaluation metrics, and open-source implementations to address experimentation settings are also summarized. This is followed by a discussion on the practical applications of community detection in various domains. The survey concludes with suggestions of challenging topics that would make for fruitful future research directions in this fast-growing deep learning field.
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Su, X., Xue, S., Liu, F., Wu, J., Yang, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Jin, D., Sheng, Q. Z., & Yu, P. S. (2022). A Comprehensive Survey on Community Detection With Deep Learning. IEEE Transactions on Neural Networks and Learning Systems, 35(4), 4682–4702. https://doi.org/10.1109/tnnls.2021.3137396
Su, Xing, et al. "A Comprehensive Survey on Community Detection With Deep Learning." IEEE Transactions on Neural Networks and Learning Systems, vol. 35, no. 4, 2022, pp. 4682–4702, https://doi.org/10.1109/tnnls.2021.3137396.
Su, Xing, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, et al. "A Comprehensive Survey on Community Detection With Deep Learning." IEEE Transactions on Neural Networks and Learning Systems 35, no. 4 (2022): 4682–4702. https://doi.org/10.1109/tnnls.2021.3137396.
Su X., Xue S., Liu F. [и др.] A Comprehensive Survey on Community Detection With Deep Learning // IEEE Transactions on Neural Networks and Learning Systems. — 2022. — Vol. 35, № 4. — P. 4682–4702. — URL: https://doi.org/10.1109/tnnls.2021.3137396
@article{su2022comprehensive,
title = {{A Comprehensive Survey on Community Detection With Deep Learning}},
author = {Xing Su and Shan Xue and Fanzhen Liu and Jia Wu and Jian Yang and Chuan Zhou and Wenbin Hu and Cecile Paris and Surya Nepal and Di Jin and Quan Z. Sheng and Philip S. Yu},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
year = {2022},
volume = {35},
number = {4},
pages = {4682--4702},
doi = {10.1109/tnnls.2021.3137396},
url = {https://doi.org/10.1109/tnnls.2021.3137396}
}TY - JOUR AU - Su, Xing AU - Xue, Shan AU - Liu, Fanzhen AU - Wu, Jia AU - Yang, Jian AU - Zhou, Chuan AU - Hu, Wenbin AU - Paris, Cecile AU - Nepal, Surya AU - Jin, Di AU - Sheng, Quan Z. AU - Yu, Philip S. TI - A Comprehensive Survey on Community Detection With Deep Learning JO - IEEE Transactions on Neural Networks and Learning Systems VL - 35 IS - 4 SP - 4682 EP - 4702 PY - 2022 DA - 2022/03/09 DO - 10.1109/tnnls.2021.3137396 UR - https://doi.org/10.1109/tnnls.2021.3137396 ER -
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