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Ksantini, \u201cRethinking graph auto-encoder models for attributed graph clustering,\u201d IEEE Trans. Knowl. Data Eng., vol.35, no.9, pp.9037-9053, Sept. 2023. 10.1109\/tkde.2022.3220948","DOI":"10.1109\/TKDE.2022.3220948"},{"key":"8","unstructured":"[8] B. Yang, X. Fu, N.D. Sidiropoulos, and M. Hong, \u201cTowards K-means-friendly spaces: Simultaneous deep learning and clustering,\u201d Proc. 34th Int. Conf. on Machine Learning, pp.3861-3870, Aug. 2017."},{"key":"9","unstructured":"[9] J. Xie, R.B. Girshick, and A. Farhadi, \u201cUnsupervised deep embedding for clustering analysis,\u201d Proc. 33rd Int. Conf. on Machine Learning, pp.478-487, June 2016."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] X. Guo, L. Gao, X. Liu, and J. Yin, \u201cImproved deep embedded clustering with local structure preservation,\u201d Proc. 26th Int. 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Toutanova, \u201cBERT: Pre-training of deep bidirectional transformers for language understanding,\u201d Proc. 2019 Conf. of the North American Chapter of the Association for Computational Linguistics, pp.4171-4186, June 2019. 10.18653\/v1\/N19-1423"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] K. He, X. Chen, S. Xie, Y. Li, P. Doll\u00e1r, and R.B. Girshick, \u201cMasked autoencoders are scalable vision learners,\u201d Proc. IEEE\/CVF Conf. on Computer Vision and Pattern Recognition, pp.15979-15988, June 2022. 10.1109\/cvpr52688.2022.01553","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"16","doi-asserted-by":"publisher","unstructured":"[16] S. Ding, B. Wu, X. Xu, L. Guo, and L. Ding, \u201cGraph clustering network with structure embedding enhanced,\u201d Pattern Recognition, vol.144, p.109833, July 2023. 10.1016\/j.patcog.2023.109833","DOI":"10.1016\/j.patcog.2023.109833"},{"key":"17","doi-asserted-by":"publisher","unstructured":"[17] D. Cai, X. He, and J. Han, \u201cLocally consistent concept factorization for document clustering,\u201d IEEE Trans. Knowl. Data Eng., vol.23, no.6, pp.902-913, June 2011. 10.1109\/tkde.2010.165","DOI":"10.1109\/TKDE.2010.165"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] K.Y. Yeung and W.L. Ruzzo, \u201cDetails of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data,\u201d Bioinformatics, vol.17, no.9, pp.763-774, Sept. 2001. 10.1093\/bioinformatics\/17.9.763","DOI":"10.1093\/bioinformatics\/17.9.763"},{"key":"19","unstructured":"[19] J.O. Palacio-Ni\u00f1o and F. Berzal, \u201cEvaluation metrics for unsupervised learning algorithms,\u201d arXiv preprint arXiv:1905.05667, May 2019. 10.48550\/arXiv.1905.05667"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] Z. Peng, H. Liu, Y. Jia, and J. Hou, \u201cAttention-driven graph clustering network,\u201d Proc. 29th ACM Int. 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