$\left[\mathrm{AAD}^{+}23\right]$  Vikrant Ashvinkumar, Sepehr Assadi, Chengyuan Deng, Jie Gao, and Chen Wang. Evaluating stability in massive social networks: Efficient streaming algorithms for structural balance. In Nicole Megow and Adam D. Smith, editors, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, APPROX/RANDOM 2023, September 11-13, 2023, Atlanta, Georgia, USA, volume 275 of LIPics, pages 58:1-58:23. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2023.
[Abb17a] Emmanuel Abbe. Community detection and stochastic block models: Recent developments. J. Mach. Learn. Res., 18:177:1-177:86, 2017.
[Abb17b] Emmanuel Abbe. Community detection and stochastic block models: recent developments. The Journal of Machine Learning Research, 18(1):6446-6531, 2017.
[ABH16] Emmanuel Abbe, Afonso S. Bandeira, and Georgina Hall. Exact recovery in the stochastic block model. IEEE Trans. Inf. Theory, 62(1):471-487, 2016.
[ACG+15] Kook Jin Ahn, Graham Cormode, Sudipto Guha, Andrew McGregor, and Anthony Wirth. Correlation clustering in data streams. In Francis R. Bach and David M. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, volume 37 of JMLR Workshop and Conference Proceedings, pages 2237-2246. JMLR.org, 2015.
$\left[\mathrm{ACL}^{+}22\right]$  Sepehr Assadi, Vaggos Chatziafratis, Jakub Lacki, Vahab Mirrokni, and Chen Wang. Hierarchical clustering in graph streams: Single-pass algorithms and space lower bounds. In Po-Ling Loh and Maxim Raginsky, editors, Conference on Learning Theory, 2-5 July 2022, London, UK, volume 178 of Proceedings of Machine Learning Research, pages 4643-4702. PMLR, 2022.
[ACN05] Nir Ailon, Moses Charikar, and Alantha Newman. Aggregating inconsistent information: ranking and clustering. In Harold N. Gabow and Ronald Fagin, editors, Proceedings of the 37th Annual ACM Symposium on Theory of Computing, Baltimore, MD, USA, May 22-24, 2005, pages 684-693. ACM, 2005.
[AEKM20] Sara Ahmadian, Alessandro Epasto, Ravi Kumar, and Mohammad Mahdian. Fair correlation clustering. In Silvia Chiappa and Roberto Calandra, editors, The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, 26-28 August 2020, Online [Palermo, Sicily, Italy], volume 108 of Proceedings of Machine Learning Research, pages 4195-4205. PMLR, 2020.
[AW22] Sepehr Assadi and Chen Wang. Sublinear time and space algorithms for correlation clustering via sparse-dense decompositions. In Mark Braverman, editor, 13th Innovations in Theoretical Computer Science Conference, ITCS 2022, January 31 - February 3, 2022, Berkeley, CA, USA, volume 215 of LIPics, pages 10:1-10:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2022.
[BBC04] Nikhil Bansal, Avrim Blum, and Shuchi Chawla. Correlation clustering. Mach. Learn., 56(1-3):89-113, 2004.
[BCMT22] Soheil Behneshad, Moses Charikar, Weiyun Ma, and Li-Yang Tan. Almost 3-approximate correlation clustering in constant rounds. In 63rd IEEE Annual Symposium on Foundations of Computer Science, FOCS 2022, Denver, CO, USA, October 31 - November 3, 2022, pages 720-731. IEEE, 2022.
[BCMT23] Soheil Behnezhad, Moses Charikar, Weiyun Ma, and Li-Yang Tan. Single-pass streaming algorithms for correlation clustering. In Proceedings of the 34th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2023 (to appear), 2023.
[BGK13] Francesco Bonchi, David García-Soriano, and Konstantin Kutzkov. Local correlation clustering. CoRR, abs/1312.5105, 2013.