{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:44:02Z","timestamp":1772693042911,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T00:00:00Z","timestamp":1750118400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"The research is partially supported by \"Pioneer\u201dand\u201cLeading Goose\u201dR&D Program of Zhejiang\"","award":["2023C01029"],"award-info":[{"award-number":["2023C01029"]}]},{"name":"The research is partially supported by \"Pioneer\u201dand\u201cLeading Goose\u201dR&D Program of Zhejiang\"","award":["2023C01029"],"award-info":[{"award-number":["2023C01029"]}]},{"name":"The research is partially supported by \"Pioneer\u201dand\u201cLeading Goose\u201dR&D Program of Zhejiang\"","award":["2023C01029"],"award-info":[{"award-number":["2023C01029"]}]},{"name":"The research is partially supported by \"Pioneer\u201dand\u201cLeading Goose\u201dR&D Program of Zhejiang\"","award":["2023C01029"],"award-info":[{"award-number":["2023C01029"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s10994-025-06809-x","type":"journal-article","created":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T10:57:54Z","timestamp":1750157874000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["HE-GAD: a behavior-enhanced contrastive learning framework for graph anomaly detection"],"prefix":"10.1007","volume":"114","author":[{"given":"Ling","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhitao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,17]]},"reference":[{"key":"6809_CR1","unstructured":"Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., & Saunshi, N. (2019). A Theoretical Analysis of Contrastive Unsupervised Representation Learning. Preprint at https:\/\/arxiv.org\/abs\/1902.09229v1"},{"key":"6809_CR2","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-66981-2_10","volume-title":"Mining Data for Financial Applications - 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers","author":"L Bellomarini","year":"2020","unstructured":"Bellomarini, L., Magnanimi, D., Nissl, M., & Sallinger, E. (2020). Neither in the programs nor in the data: Mining the hidden financial knowledge with knowledge graphs and reasoning. In V. Bitetta, I. Bordino, A. Ferretti, F. Gullo, G. Ponti, & L. Severini (Eds.), Mining Data for Financial Applications - 5th ECML PKDD Workshop, MIDAS 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers (Vol. 12591, pp. 119\u2013134). Springer."},{"key":"6809_CR3","doi-asserted-by":"publisher","first-page":"49114","DOI":"10.1109\/ACCESS.2023.3275789","volume":"11","author":"T Bilot","year":"2023","unstructured":"Bilot, T., El Madhoun, N., Al Agha, K., & Zouaoui, A. (2023). Graph neural networks for intrusion detection: A survey. IEEE Access, 11, 49114\u201349139.","journal-title":"IEEE Access"},{"key":"6809_CR4","first-page":"93","volume-title":"LOF: Identifying density-based local outliers","author":"MM Breunig","year":"2000","unstructured":"Breunig, M. M., Kriegel, H., Ng, R. T., & Sander, J. (2000). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. In W. Chen, J. F. Naughton, & P. A. Bernstein (Eds.), LOF: Identifying density-based local outliers (pp. 93\u2013104). ACM."},{"key":"6809_CR5","doi-asserted-by":"crossref","unstructured":"Ding, K., Li, J., Bhanushali, R., & Liu, H. (2019). Deep anomaly detection on attributed networks. In T. Y. Berger-Wolf & N. V. Chawla (Eds.), SIAM International Conference on Data Mining (SDM) (pp. 594\u2013602). Philadelphia, PA: SIAM.","DOI":"10.1137\/1.9781611975673.67"},{"key":"6809_CR6","unstructured":"Duan, J., Wang, S., Zhang, P., Zhu, E., Hu, J., Jin, H., Liu, Y., & Dong, Z. (2022). Graph Anomaly Detection via Multi-Scale Contrastive Learning Networks with Augmented View. Preprint at https:\/\/arxiv.org\/abs\/2212.00535"},{"issue":"12","key":"6809_CR7","doi-asserted-by":"publisher","first-page":"18172","DOI":"10.1109\/TNNLS.2023.3312655","volume":"35","author":"J Duan","year":"2024","unstructured":"Duan, J., Xiao, B., Wang, S., Zhou, H., & Liu, X. (2024). Arise: Graph anomaly detection on attributed networks via substructure awareness. IEEE Transactions on Neural Networks and Learning Systems, 35(12), 18172\u201318185.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"6809_CR8","doi-asserted-by":"crossref","unstructured":"Duan, J., Zhang, P., Wang, S., Hu, J., Jin, H., Zhang, J., Zhou, H., & Liu, X. (2023). Proceedings of the 31st ACM International Conference on Multimedia. In A. El-Saddik, T. Mei, R. Cucchiara, M. Bertini, D. P. T. Vallejo, P. K. Atrey, & M. S. Hossain (Eds.), Normality learning-based graph anomaly detection via multi-scale contrastive learning (pp. 7502\u20137511). New York, NY, USA: Association for Computing Machinery.","DOI":"10.1145\/3581783.3612064"},{"issue":"1","key":"6809_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/00401706.1969.10490657","volume":"11","author":"FE Grubbs","year":"1969","unstructured":"Grubbs, F. E. (1969). Procedures for detecting outlying observations in samples. Technometrics, 11(1), 1\u201321.","journal-title":"Technometrics"},{"issue":"4","key":"6809_CR10","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1093\/bioinformatics\/btt717","volume":"30","author":"T Hocevar","year":"2014","unstructured":"Hocevar, T., & Demsar, J. (2014). A combinatorial approach to graphlet counting. Bioinformatics., 30(4), 559\u2013565.","journal-title":"Bioinformatics."},{"key":"6809_CR11","doi-asserted-by":"crossref","unstructured":"Jin, M., Liu, Y., Zheng, Y., Chi, L., Li, Y., & Pan, S. (2021). Proceedings of the 30th ACM International Conference on Information & Knowledge Management. In G. Demartini, G. Zuccon, J. S. Culpepper, Z. Huang, & H. Tong (Eds.), ANEMONE: graph anomaly detection with multi-scale contrastive learning (pp. 3122\u20133126). New York, NY, USA: ACM.","DOI":"10.1145\/3459637.3482057"},{"key":"6809_CR12","doi-asserted-by":"publisher","first-page":"111820","DOI":"10.1109\/ACCESS.2022.3211306","volume":"10","author":"H Kim","year":"2022","unstructured":"Kim, H., Lee, B. S., Shin, W.-Y., & Lim, S. (2022). Graph anomaly detection with graph neural networks: Current status and challenges. IEEE Access, 10, 111820\u2013111829.","journal-title":"IEEE Access"},{"key":"6809_CR13","unstructured":"Kingma, D.P., & Ba, J. (2017). Adam: A Method for Stochastic Optimization. Preprint at https:\/\/arxiv.org\/abs\/1412.6980v9"},{"key":"6809_CR14","unstructured":"Kipf, T.N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. Preprint at https:\/\/arxiv.org\/abs\/1609.02907v4"},{"key":"6809_CR15","doi-asserted-by":"crossref","unstructured":"Kuchaiev, O., Milenkovic, T., Memisevic, V., Hayes, W., & Przulj, N. (2009). Topological network alignment uncovers biological function and phylogeny. Preprint at https:\/\/arxiv.org\/abs\/0810.3280v4","DOI":"10.1038\/npre.2009.4089.1"},{"key":"6809_CR16","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., & Leskovec, J. (2019). Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019. In A. Teredesai, V. Kumar, Y. Li, R. Rosales, E. Terzi, & G. Karypis (Eds.), Predicting dynamic embedding trajectory in temporal interaction networks (pp. 1269\u20131278). New York, NY, USA: ACM.","DOI":"10.1145\/3292500.3330895"},{"key":"6809_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, X., Li, J., Du, M., & Zou, N. (2019). Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3-7, 2019. In W. Zhu, D. Tao, X. Cheng, P. Cui, E. A. Rundensteiner, D. Carmel, Q. He, & J. X. Yu (Eds.), Specae: Spectral autoencoder for anomaly detection in attributed networks (pp. 2233\u20132236). New York, NY, USA: ACM.","DOI":"10.1145\/3357384.3358074"},{"key":"6809_CR18","doi-asserted-by":"crossref","unstructured":"Li, A., Qin, Z., Liu, R., Yang, Y., & Li, D. (2019). Proceedings of the 28th ACM International Conference on Information and Knowledge Management. In W. Zhu, D. Tao, X. Cheng, P. Cui, E. A. Rundensteiner, D. Carmel, Q. He, & J. X. Yu (Eds.), Spam review detection with graph convolutional networks (pp. 2703\u20132711). New York, NY, USA: Association for Computing Machinery.","DOI":"10.1145\/3357384.3357820"},{"key":"6809_CR19","doi-asserted-by":"crossref","unstructured":"Liu, Z., Dou, Y., Yu, P. S., Deng, Y., & Peng, H. (2020). Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. In J. X. Huang, Y. Chang, X. Cheng, J. Kamps, V. Murdock, J. Wen, & Y. Liu (Eds.), Alleviating the inconsistency problem of applying graph neural network to fraud detection (pp. 1569\u20131572). New York, NY, USA: Association for Computing Machinery.","DOI":"10.1145\/3397271.3401253"},{"issue":"6","key":"6809_CR20","first-page":"5879","volume":"35","author":"Y Liu","year":"2023","unstructured":"Liu, Y., Jin, M., Pan, S., Zhou, C., Zheng, Y., Xia, F., & Yu, P. S. (2023). Graph self-supervised learning: A survey. IEEE Transactions on Knowledge and Data Engineering., 35(6), 5879\u20135900.","journal-title":"IEEE Transactions on Knowledge and Data Engineering."},{"issue":"6","key":"6809_CR21","doi-asserted-by":"publisher","first-page":"2378","DOI":"10.1109\/TNNLS.2021.3068344","volume":"33","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Li, Z., Pan, S., Gong, C., Zhou, C., & Karypis, G. (2022). Anomaly detection on attributed networks via contrastive self-supervised learning. IEEE Transactions on Neural Networks and Learning Systems., 33(6), 2378\u20132392.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems."},{"key":"6809_CR22","doi-asserted-by":"publisher","first-page":"390","DOI":"10.1007\/978-3-642-23783-6_25","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"D Luo","year":"2011","unstructured":"Luo, D., Ding, C. H. Q., & Huang, H. (2011). Graph evolution via social diffusion processes. In D. Gunopulos, T. Hofmann, D. Malerba, & M. Vazirgiannis (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. 6912, pp. 390\u2013404). Springer."},{"issue":"12","key":"6809_CR23","doi-asserted-by":"publisher","first-page":"12012","DOI":"10.1109\/TKDE.2021.3118815","volume":"35","author":"X Ma","year":"2021","unstructured":"Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., Xiong, H., & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012\u201312038.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"6809_CR24","unstructured":"Oord, A., Li, Y., & Vinyals, O. (2019). Representation Learning with Contrastive Predictive Coding. Preprint at https:\/\/arxiv.org\/abs\/1807.03748v2"},{"key":"6809_CR25","doi-asserted-by":"crossref","unstructured":"Pan, J., Liu, Y., Zheng, Y. & Pan, S. (2023). PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection. Preprint at https:\/\/arxiv.org\/abs\/2310.11676v3","DOI":"10.1109\/ICDM58522.2023.00157"},{"key":"6809_CR26","doi-asserted-by":"crossref","unstructured":"Perozzi, B., & Akoglu, L. (2016). Scalable Anomaly Ranking of Attributed Neighborhoods. Preprint at https:\/\/arxiv.org\/abs\/1601.06711v1","DOI":"10.1137\/1.9781611974348.24"},{"key":"6809_CR27","unstructured":"Platonov, O., Kuznedelev, D., Diskin, M., Babenko, A., & Prokhorenkova, L. (2024). A critical look at the evaluation of GNNs under heterophily: Are we really making progress? Preprint at https:\/\/arxiv.org\/abs\/2302.11640v2"},{"key":"6809_CR28","first-page":"69","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"L Potin","year":"2023","unstructured":"Potin, L., Figueiredo, R., Labatut, V., & Largeron, C. (2023). Pattern mining for anomaly detection in graphs: Application to fraud in public procurement. In G. D. F. Morales, C. Perlich, N. Ruchansky, N. Kourtellis, E. Baralis, & F. Bonchi (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. 14174, pp. 69\u201387). Springer."},{"key":"6809_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2020.113303","volume":"133","author":"T Pourhabibi","year":"2020","unstructured":"Pourhabibi, T., Ong, K.-L., Kam, B. H., & Boo, Y. L. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems, 133, Article 113303.","journal-title":"Decision Support Systems"},{"issue":"2","key":"6809_CR30","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1093\/bioinformatics\/btl301","volume":"23","author":"N Pr\u017eulj","year":"2007","unstructured":"Pr\u017eulj, N. (2007). Biological network comparison using graphlet degree distribution. Bioinformatics, 23(2), 177\u2013183.","journal-title":"Bioinformatics"},{"issue":"18","key":"6809_CR31","doi-asserted-by":"publisher","first-page":"3508","DOI":"10.1093\/bioinformatics\/bth436","volume":"20","author":"N Przulj","year":"2004","unstructured":"Przulj, N., Corneil, D. G., & Jurisica, I. (2004). Modeling interactome: Scale-free or geometric? Bioinformatics., 20(18), 3508\u20133515.","journal-title":"Bioinformatics."},{"key":"6809_CR32","unstructured":"Tang, J., Hua, F., Gao, Z., Zhao, P. & Li, J. (2023). GADBench: Revisiting and Benchmarking Supervised Graph Anomaly Detection. Preprint at https:\/\/arxiv.org\/abs\/2306.12251v2"},{"key":"6809_CR33","unstructured":"Tang, J., Li, J., Gao, Z. & Li, J. (2022). Rethinking Graph Neural Networks for Anomaly Detection. Preprint at https:\/\/arxiv.org\/abs\/2205.15508v1"},{"key":"6809_CR34","first-page":"418","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"W Tao","year":"2024","unstructured":"Tao, W., Zhu, H., Tan, K., Wang, J., Liang, Y., Jiang, H., Yuan, P., & Lan, Y. (2024). Finqa: A training-free dynamic knowledge graph question answering system in finance with llm-based revision. In A. Bifet, P. Daniusis, J. Davis, T. Krilavicius, M. Kull, E. Ntoutsi, K. Puolam\u00e4ki, & I. Zliobaite (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. 14948, pp. 418\u2013423). Springer."},{"key":"6809_CR35","first-page":"776","volume-title":"Computer Vision - ECCV 2020\u201316th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI","author":"Y Tian","year":"2020","unstructured":"Tian, Y., Krishnan, D., & Isola, P. (2020). Contrastive multiview coding. In A. Vedaldi, H. Bischof, T. Brox, & J. Frahm (Eds.), Computer Vision - ECCV 2020\u201316th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XI (Vol. 12356, pp. 776\u2013794). Springer."},{"key":"6809_CR36","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P. & Bengio, Y. (2018). Graph Attention Networks. Preprint at https:\/\/arxiv.org\/abs\/1710.10903v3"},{"key":"6809_CR37","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/978-3-030-46150-8_26","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"M Vijaikumar","year":"2019","unstructured":"Vijaikumar, M., Shevade, S. K., & Murty, M. N. (2019). Sorecgat: Leveraging graph attention mechanism for top-n social recommendation. In U. Brefeld, \u00c9. Fromont, A. Hotho, A. J. Knobbe, M. H. Maathuis, & C. Robardet (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. 11906, pp. 430\u2013446). Springer."},{"key":"6809_CR38","unstructured":"Weber, M., Domeniconi, G., Chen, J., Weidele, D.K.I., Bellei, C., Robinson, T., & Leiserson, C.E. (2019). Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics. Preprint at https:\/\/arxiv.org\/abs\/1908.02591v1"},{"key":"6809_CR39","unstructured":"Wu, Z., Xiong, Y., Yu, S., & Lin, D. (2018). Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination. Preprint at https:\/\/arxiv.org\/abs\/1805.01978v1"},{"issue":"2","key":"6809_CR40","doi-asserted-by":"publisher","first-page":"2412","DOI":"10.1109\/TPAMI.2022.3170559","volume":"45","author":"Y Xie","year":"2023","unstructured":"Xie, Y., Xu, Z., Zhang, J., Wang, Z., & Ji, S. (2023). Self-supervised learning of graph neural networks: A unified review. IEEE Transactions on Pattern Analysis and Machine Intelligence., 45(2), 2412\u20132429.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence."},{"key":"6809_CR41","unstructured":"Xu, K., Hu, W., Leskovec, J., & Jegelka, S. (2019). How Powerful are Graph Neural Networks? Preprint at https:\/\/arxiv.org\/abs\/1810.00826v3"},{"key":"6809_CR42","first-page":"337","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","author":"F Xu","year":"2024","unstructured":"Xu, F., Wang, N., Wu, H., Wen, X., Zhang, D., Lu, S., Li, B., Gong, W., Wan, H., & Zhao, X. (2024). Gladformer: A mixed perspective for graph-level anomaly detection. In A. Bifet, J. Davis, T. Krilavicius, M. Kull, E. Ntoutsi, & I. Zliobaite (Eds.), Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Vol. 14946, pp. 337\u2013353). Springer."},{"key":"6809_CR43","doi-asserted-by":"crossref","unstructured":"Zhang, J., Wang, S., & Chen, S. (2022). Reconstruction Enhanced Multi-View Contrastive Learning for Anomaly Detection on Attributed Networks. Preprint at https:\/\/arxiv.org\/abs\/2205.04816v1","DOI":"10.24963\/ijcai.2022\/330"},{"issue":"12","key":"6809_CR44","doi-asserted-by":"publisher","first-page":"12220","DOI":"10.1109\/TKDE.2021.3119326","volume":"35","author":"Y Zheng","year":"2023","unstructured":"Zheng, Y., Jin, M., Liu, Y., Chi, L., Phan, K. T., & Chen, Y. P. (2023). Generative and contrastive self-supervised learning for graph anomaly detection. IEEE Transactions on Knowledge and Data Engineering., 35(12), 12220\u201312233.","journal-title":"IEEE Transactions on Knowledge and Data Engineering."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-025-06809-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-025-06809-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-025-06809-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T19:41:34Z","timestamp":1757187694000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-025-06809-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,17]]},"references-count":44,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["6809"],"URL":"https:\/\/doi.org\/10.1007\/s10994-025-06809-x","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,17]]},"assertion":[{"value":"11 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"169"}}