{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T08:55:43Z","timestamp":1781340943134,"version":"3.54.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T00:00:00Z","timestamp":1780012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s00607-026-01627-y","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T06:36:49Z","timestamp":1780036609000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Fake detection in imbalance dataset by semi-supervised learning with GAN"],"prefix":"10.1007","volume":"108","author":[{"given":"Jinus","family":"Bordbar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saman","family":"Ardalan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammadreza","family":"Mohammadrezaei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahra","family":"Ghasemi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,29]]},"reference":[{"key":"1627_CR1","doi-asserted-by":"publisher","unstructured":"Agrawal A (2020) Hamling T Sentiment analysis of tweets to gain insights into the 2016 us election https:\/\/doi.org\/10.52214\/cusj.v11i.6359","DOI":"10.52214\/cusj.v11i.6359"},{"key":"1627_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.procs.2020.08.020","volume":"176","author":"MO Kaplan","year":"2020","unstructured":"Kaplan MO, Alptekin SE (2020) An improved bigan based approach for anomaly detection. Procedia Computer Science 176:185\u2013194. https:\/\/doi.org\/10.1016\/j.procs.2020.08.020","journal-title":"Procedia Computer Science"},{"key":"1627_CR3","doi-asserted-by":"publisher","unstructured":"Kim J, Jeong K, Choi H, Seo K (2020) Gan-based anomaly detection in imbalance problems. In: European Conference on Computer Vision, pp. 128\u2013145. https:\/\/doi.org\/10.1109\/IJCNN.2011.6033365 . Springer","DOI":"10.1109\/IJCNN.2011.6033365"},{"issue":"3","key":"1627_CR4","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/s13278-012-0090-8","volume":"3","author":"CG Akcora","year":"2013","unstructured":"Akcora CG, Carminati B, Ferrari E (2013) User similarities on social networks. Soc Netw Anal Min 3(3):475\u2013495. https:\/\/doi.org\/10.1007\/s13278-012-0090-8","journal-title":"Soc Netw Anal Min"},{"issue":"1","key":"1627_CR5","doi-asserted-by":"publisher","first-page":"18001","DOI":"10.1209\/0295-5075\/89\/18001","volume":"89","author":"L L\u00fc","year":"2010","unstructured":"L\u00fc L (2010) Zhou T Link prediction in weighted networks: The role of weak ties. EPL (Europhysics Letters) 89(1):18001. https:\/\/doi.org\/10.1209\/0295-5075\/89\/18001","journal-title":"EPL (Europhysics Letters)"},{"key":"1627_CR6","unstructured":"Baldi P (2012) Autoencoders, unsupervised learning, and deep architectures. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 37\u201349. JMLR Workshop and Conference Proceedings. https:\/\/api.semanticscholar.org\/CorpusID:10921035"},{"key":"1627_CR7","unstructured":"Learning S-S (2006) Semi-supervised learning. CSZ2006. html"},{"key":"1627_CR8","doi-asserted-by":"publisher","unstructured":"Mohammadrezaei M, Shiri ME, Rahmani AM (2018) Identifying fake accounts on social networks based on graph analysis and classification algorithms. Security and Communication Networks 2018. https:\/\/doi.org\/10.1155\/2018\/5923156","DOI":"10.1155\/2018\/5923156"},{"key":"1627_CR9","doi-asserted-by":"publisher","unstructured":"Meng Q, Catchpoole D, Skillicom D, Kennedy PJ (2017) Relational autoencoder for feature extraction. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 364\u2013371. https:\/\/doi.org\/10.1109\/IJCNN.2017.7965877 . IEEE","DOI":"10.1109\/IJCNN.2017.7965877"},{"key":"1627_CR10","doi-asserted-by":"publisher","unstructured":"Odena A (2016) Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583https:\/\/doi.org\/10.1109\/ISBI.2018.8363749","DOI":"10.1109\/ISBI.2018.8363749"},{"issue":"11","key":"1627_CR11","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A (2020) Bengio Y Generative adversarial networks. Commun ACM 63(11):139\u2013144. https:\/\/doi.org\/10.1145\/3422622","journal-title":"Commun ACM"},{"issue":"1","key":"1627_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-021-00742-2","volume":"11","author":"P Wanda","year":"2021","unstructured":"Wanda P, Jie HJ (2021) Deepfriend: finding abnormal nodes in online social networks using dynamic deep learning. Soc Netw Anal Min 11(1):1\u201312. https:\/\/doi.org\/10.1007\/s13278-021-00742-2","journal-title":"Soc Netw Anal Min"},{"key":"1627_CR13","doi-asserted-by":"crossref","unstructured":"Bordbar J, Mohammadrezaie M, Ardalan S, Shiri ME (2022) Detecting fake accounts through generative adversarial network in online social media. arXiv preprint arXiv:2210.15657","DOI":"10.21203\/rs.3.rs-3710452\/v1"},{"key":"1627_CR14","unstructured":"Donahue J, Kr\u00e4henb\u00fchl P (2016) Darrell T Adversarial feature learning. arXiv preprint arXiv:1605.09782"},{"key":"1627_CR15","doi-asserted-by":"crossref","unstructured":"Tavallaee M, Bagheri E, Lu W, Ghorbani AA (2009) A detailed analysis of the kdd cup 99 data set. In: 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1\u20136. Ieee","DOI":"10.1109\/CISDA.2009.5356528"},{"issue":"4","key":"1627_CR16","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST (1998) Osuna E, Platt J, Scholkopf B Support vector machines. IEEE Intelligent Systems and their applications 13(4):18\u201328","journal-title":"IEEE Intelligent Systems and their applications"},{"key":"1627_CR17","doi-asserted-by":"publisher","DOI":"10.1002\/9781118548387","volume-title":"Applied Logistic Regression.","author":"DW Hosmer Jr","year":"2013","unstructured":"Hosmer DW Jr, Lemeshow S, Sturdivant RX (2013) Applied Logistic Regression. John Wiley & Sons, New York"},{"issue":"7","key":"1627_CR18","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1162\/089976603321891855","volume":"15","author":"SS Keerthi","year":"2003","unstructured":"Keerthi SS, Lin C-J (2003) Asymptotic behaviors of support vector machines with gaussian kernel. Neural Comput 15(7):1667\u20131689","journal-title":"Neural Comput"},{"key":"1627_CR19","unstructured":"Salimans T, Goodfellow IJ, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. ArXiv abs\/1606.03498"},{"key":"1627_CR20","doi-asserted-by":"publisher","unstructured":"Jouili S, Tabbone S, Valveny E (2009) Comparing graph similarity measures for graphical recognition. In: International Workshop on Graphics Recognition, pp. 37\u201348. https:\/\/doi.org\/10.1007\/978-3-642-13728-0_4 . Springer","DOI":"10.1007\/978-3-642-13728-0_4"},{"key":"1627_CR21","unstructured":"Santisteban J (2015) Tejada-C\u00e1rcamo J Unilateral jaccard similarity coefficient"},{"key":"1627_CR22","doi-asserted-by":"publisher","unstructured":"Dong L, Li Y, Yin H, Le H, Rui M (2013) The algorithm of link prediction on social network. Math Probl Eng 2013. https:\/\/doi.org\/10.1155\/2013\/125123","DOI":"10.1155\/2013\/125123"},{"key":"1627_CR23","doi-asserted-by":"publisher","unstructured":"Benesty J, Chen J, Huang Y, Cohen I (2009). Pearson correlation coefficient. https:\/\/doi.org\/10.1007\/978-3-642-00296-0_5","DOI":"10.1007\/978-3-642-00296-0_5"},{"issue":"3","key":"1627_CR24","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/978-3-642-00296-0_5","volume":"129","author":"KL Elmore","year":"2001","unstructured":"Elmore KL, Richman MB (2001) Euclidean distance as a similarity metric for principal component analysis. Mon Weather Rev 129(3):540\u2013549. https:\/\/doi.org\/10.1007\/978-3-642-00296-0_5","journal-title":"Mon Weather Rev"},{"issue":"9","key":"1627_CR25","doi-asserted-by":"publisher","first-page":"1672","DOI":"10.1109\/TPAMI.2008.114","volume":"30","author":"N Kwak","year":"2008","unstructured":"Kwak N (2008) Principal component analysis based on l1-norm maximization. IEEE Trans Pattern Anal Mach Intell 30(9):1672\u20131680. https:\/\/doi.org\/10.1109\/TPAMI.2008.114","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1627_CR26","doi-asserted-by":"publisher","unstructured":"Cukierski W, Hamner B, Yang B (2011) Graph-based features for supervised link prediction. In: The 2011 International Joint Conference on Neural Networks, pp. 1237\u20131244. https:\/\/doi.org\/10.1109\/IJCNN.2011.6033365 . IEEE","DOI":"10.1109\/IJCNN.2011.6033365"},{"issue":"9","key":"1627_CR27","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia E (2009) A Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284. https:\/\/doi.org\/10.1109\/TKDE.2008.239","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"1627_CR28","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/S0034-4257(97)00083-7","volume":"62","author":"SV Stehman","year":"1997","unstructured":"Stehman SV (1997) Selecting and interpreting measures of thematic classification accuracy. Remote Sens Environ 62(1):77\u201389. https:\/\/doi.org\/10.1016\/S0034-4257(97)00083-7","journal-title":"Remote Sens Environ"},{"key":"1627_CR29","doi-asserted-by":"publisher","unstructured":"Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning, pp. 233\u2013240 . https:\/\/doi.org\/10.1145\/1143844.1143874","DOI":"10.1145\/1143844.1143874"},{"key":"1627_CR31","doi-asserted-by":"publisher","unstructured":"Zhou, M., Feng, W., Zhu, Y., Zhang, D., Dong, Y., Tang, J.: Semi-supervised social bot detection with initial residual relation attention networks. In: Joint European Conference on MachineLearning and Knowledge Discovery in Databases, pp. 207\u2013224 (2023). Springer https:\/\/doi.org\/10.1007\/978-3-031-43427-3_13","DOI":"10.1007\/978-3-031-43427-3_13"},{"key":"1627_CR32","doi-asserted-by":"publisher","unstructured":"Gui, Q., Zhou, H., Guo, N., Niu, B.: A survey of class-imbalanced semi-supervised learning.Machine Learning 113(8), 5057\u20135086 (2024) https:\/\/doi.org\/10.1007\/s10994-023-06344-7","DOI":"10.1007\/s10994-023-06344-7"},{"key":"1627_CR33","doi-asserted-by":"publisher","unstructured":"Liu, Y., Wen, C.: A new method of semi-supervised learning classification based on multi-modeaugmentation in small labeled sample environment. Scientific Reports 15(1), 22022 (2025) https:\/\/doi.org\/10.1038\/s41598-025-02324-0","DOI":"10.1038\/s41598-025-02324-0"},{"key":"1627_CR34","doi-asserted-by":"publisher","unstructured":"Lou, Y., Liu, J., Sheng, Y., Wang, J., Zhang, Y., Ren, Y.: Addressing class imbalance with probabilistic graphical models and variational inference. In: 2025 5th International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA), pp. 1238\u20131242 (2025).IEEE https:\/\/doi.org\/10.1109\/AIITA65135.2025.11047653","DOI":"10.1109\/AIITA65135.2025.11047653"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-026-01627-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-026-01627-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-026-01627-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T08:12:22Z","timestamp":1781338342000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-026-01627-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,29]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["1627"],"URL":"https:\/\/doi.org\/10.1007\/s00607-026-01627-y","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,29]]},"assertion":[{"value":"9 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 June 2026","order":5,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":6,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"In this article, the affiliation of the author Mohammadreza Mohammadrezaei was incorrect. This has been corrected now.","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"In our proposed method, we represent each user as a node in the social network, characterized by a set of numbers, with connections between users depicted as edges indicating which numbers are interconnected. We acknowledge the importance of ethical considerations in handling user data within social networks. We adhere to ethical standards by ensuring the confidentiality and privacy of user information. The data used in this study is anonymized and does not include personally identifiable information. Additionally, any potential implications for user privacy have been carefully considered, and steps have been taken to mitigate any adverse effects. We are committed to conducting our research with the utmost respect for ethical guidelines and standards.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"89"}}