{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T17:28:34Z","timestamp":1767374914851,"version":"3.40.4"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72374219","71974202","72174153"],"award-info":[{"award-number":["72374219","71974202","72174153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Fund of China","doi-asserted-by":"publisher","award":["23&ZD230"],"award-info":[{"award-number":["23&ZD230"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Scientometrics"],"published-print":{"date-parts":[[2025,4]]},"DOI":"10.1007\/s11192-025-05294-2","type":"journal-article","created":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T11:18:48Z","timestamp":1744197528000},"page":"2069-2091","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A temporal evolution and fine-grained information aggregation model for citation count prediction"],"prefix":"10.1007","volume":"130","author":[{"given":"Zhengang","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7099-0853","authenticated-orcid":false,"given":"Chuanming","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingnan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"An","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,9]]},"reference":[{"issue":"1","key":"5294_CR1","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.joi.2018.11.003","volume":"13","author":"G Abramo","year":"2019","unstructured":"Abramo, G., D\u2019Angelo, C. A., & Felici, G. (2019). Predicting publication long-term impact through a combination of early citations and journal impact factor. Journal of Informetrics, 13(1), 32\u201349.","journal-title":"Journal of Informetrics"},{"issue":"2","key":"5294_CR2","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1016\/j.joi.2019.02.011","volume":"13","author":"A Abrishami","year":"2019","unstructured":"Abrishami, A., & Aliakbary, S. (2019). Predicting citation counts based on deep neural network learning techniques. Journal of Informetrics, 13(2), 485\u2013499.","journal-title":"Journal of Informetrics"},{"key":"5294_CR3","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.techfore.2019.03.002","volume":"143","author":"AC Adamuthe","year":"2019","unstructured":"Adamuthe, A. C., & Thampi, G. T. (2019). Technology forecasting: A case study of computational technologies. Technological Forecasting and Social Change, 143, 181\u2013189.","journal-title":"Technological Forecasting and Social Change"},{"issue":"8","key":"5294_CR4","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.3390\/electronics9081253","volume":"9","author":"M Afzal","year":"2020","unstructured":"Afzal, M., Park, B. J., Hussain, M., & Lee, S. (2020). Deep learning based biomedical literature classification using criteria of scientific rigor. Electronics, 9(8), 1253.","journal-title":"Electronics"},{"issue":"1","key":"5294_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11192-023-04845-9","volume":"129","author":"WSN Aiza","year":"2024","unstructured":"Aiza, W. S. N., Shuib, L., Idris, N., & Normadhi, N. B. A. (2024). Features, techniques and evaluation in predicting articles\u2019 citations: A review from years 2010\u20132023. Scientometrics, 129(1), 1\u201329.","journal-title":"Scientometrics"},{"key":"5294_CR6","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/2239152","volume":"2022","author":"YA Alohali","year":"2022","unstructured":"Alohali, Y. A., Fayed, M. S., Mesallam, T., Abdelsamad, Y., Almuhawas, F., & Hagr, A. (2022). A machine learning model to predict citation counts of scientific papers in otology field. BioMed Research International, 2022, e2239152.","journal-title":"BioMed Research International"},{"issue":"1","key":"5294_CR7","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.joi.2019.01.010","volume":"13","author":"X Bai","year":"2019","unstructured":"Bai, X., Zhang, F., & Lee, I. (2019). Predicting the citations of scholarly paper. Journal of Informetrics, 13(1), 407\u2013418.","journal-title":"Journal of Informetrics"},{"key":"5294_CR8","doi-asserted-by":"crossref","unstructured":"Cai, B., Xiang, Y., Gao, L., Zhang, H., Li, Y., & Li, J. (2022). Temporal knowledge graph completion: A survey. arXiv preprint arXiv:2201.08236.","DOI":"10.24963\/ijcai.2023\/734"},{"issue":"2","key":"5294_CR9","doi-asserted-by":"publisher","first-page":"471","DOI":"10.1016\/j.joi.2016.02.006","volume":"10","author":"X Cao","year":"2016","unstructured":"Cao, X., Chen, Y., & Ray Liu, K. J. (2016). A data analytic approach to quantifying scientific impact. Journal of Informetrics, 10(2), 471\u2013484.","journal-title":"Journal of Informetrics"},{"key":"5294_CR10","doi-asserted-by":"crossref","unstructured":"Chen, J., & Zhang, C. (2015). Predicting citation counts of papers. In Proceedings of the 14th International Conference on Cognitive Informatics & Cognitive Computing (pp. 434\u2013440). IEEE.","DOI":"10.1109\/ICCI-CC.2015.7259421"},{"issue":"4","key":"5294_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2022.101344","volume":"16","author":"A Ebadi","year":"2022","unstructured":"Ebadi, A., Auger, A., & Gauthier, Y. (2022). Detecting emerging technologies and their evolution using deep learning and weak signal analysis. Journal of Informetrics, 16(4), 101344.","journal-title":"Journal of Informetrics"},{"issue":"3","key":"5294_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2023.101413","volume":"17","author":"X Feng","year":"2023","unstructured":"Feng, X., Zhao, Q., & Zhu, R. (2023). Towards popularity prediction of information cascades via degree distribution and deep neural networks. Journal of Informetrics, 17(3), 101413.","journal-title":"Journal of Informetrics"},{"issue":"1","key":"5294_CR13","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1007\/s11192-010-0160-5","volume":"85","author":"L Fu","year":"2010","unstructured":"Fu, L., & Aliferis, C. (2010). Using content-based and bibliometric features for machine learning models to predict citation counts in the biomedical literature. Scientometrics, 85(1), 257\u2013270.","journal-title":"Scientometrics"},{"key":"5294_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121634","volume":"238","author":"T Gao","year":"2024","unstructured":"Gao, T., Liu, J., Pan, R., & Wang, H. (2024). Citation counts prediction of statistical publications based on multi-layer academic networks via neural network model. Expert Systems with Applications, 238, 121634.","journal-title":"Expert Systems with Applications"},{"issue":"5\u20136","key":"5294_CR15","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","volume":"18","author":"A Graves","year":"2005","unstructured":"Graves, A., & Schmidhuber, J. (2005). Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18(5\u20136), 602\u2013610.","journal-title":"Neural Networks"},{"issue":"4","key":"5294_CR16","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1109\/TKDE.2013.56","volume":"26","author":"Z Guo","year":"2013","unstructured":"Guo, Z., Zhang, Z. M., Zhu, S., Chi, Y., & Gong, Y. (2013). A two-level topic model towards knowledge discovery from citation networks. IEEE Transactions on Knowledge and Data Engineering, 26(4), 780\u2013794.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"6","key":"5294_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103512","volume":"60","author":"G He","year":"2023","unstructured":"He, G., Xue, Z., Jiang, Z., Kang, Y., Zhao, S., & Lu, W. (2023). H2CGL: Modeling dynamics of citation network for impact prediction. Information Processing & Management, 60(6), 103512.","journal-title":"Information Processing & Management"},{"issue":"8","key":"5294_CR18","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780.","journal-title":"Neural Computation"},{"key":"5294_CR19","unstructured":"Holm, A. N., Plank, B., Wright, D., & Augenstein, I. (2020). Longitudinal citation prediction using temporal graph neural networks. arXiv preprint arXiv:2012.05742."},{"issue":"2","key":"5294_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102799","volume":"59","author":"S Huang","year":"2022","unstructured":"Huang, S., Huang, Y., Bu, Y., Lu, W., Qian, J., & Wang, D. (2022). Fine-grained citation count prediction via a transformer-based model with among-attention mechanism. Information Processing & Management, 59(2), 102799.","journal-title":"Information Processing & Management"},{"issue":"24","key":"5294_CR21","doi-asserted-by":"publisher","first-page":"3303","DOI":"10.1093\/bioinformatics\/btp585","volume":"25","author":"A Ib\u00e1\u00f1ez","year":"2009","unstructured":"Ib\u00e1\u00f1ez, A., Larra\u00f1aga, P., & Bielza, C. (2009). Predicting citation count of Bioinformatics papers within four years of publication. Bioinformatics, 25(24), 3303\u20133309.","journal-title":"Bioinformatics"},{"key":"5294_CR22","doi-asserted-by":"crossref","unstructured":"Jiang, S., Koch, B., & Sun, Y. (2021). HINTS: Citation time series prediction for new publications via dynamic heterogeneous information network embedding. In Proceedings of the web conference 2021 (pp. 3158\u20133167). ACM.","DOI":"10.1145\/3442381.3450107"},{"issue":"3","key":"5294_CR23","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1007\/s10462-023-10657-3","volume":"57","author":"A Khatoon","year":"2024","unstructured":"Khatoon, A., Daud, A., & Amjad, T. (2024). Categorization and correlational analysis of quality factors influencing citation. Artificial Intelligence Review, 57(3), 70.","journal-title":"Artificial Intelligence Review"},{"key":"5294_CR24","unstructured":"Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907."},{"issue":"1","key":"5294_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.dim.2023.100044","volume":"8","author":"B Kutela","year":"2024","unstructured":"Kutela, B., Kadeha, C., Magehema, R. T., Avelar, R. E., & Alluri, P. (2024). Leveraging text mining approach to explore research roadmap and future direction of wrong-way driving crash studies. Data and Information Management, 8(1), 100044.","journal-title":"Data and Information Management"},{"issue":"7","key":"5294_CR26","doi-asserted-by":"publisher","first-page":"6225","DOI":"10.1007\/s11192-021-03880-8","volume":"126","author":"X Li","year":"2021","unstructured":"Li, X., Peng, S., & Du, J. (2021). Towards medical knowmetrics: Representing and computing medical knowledge using semantic predications as the knowledge unit and the uncertainty as the knowledge context. Scientometrics, 126(7), 6225\u20136251.","journal-title":"Scientometrics"},{"issue":"4","key":"5294_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2022.101333","volume":"16","author":"X Li","year":"2022","unstructured":"Li, X., Tang, X., & Cheng, Q. (2022). Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network. Journal of Informetrics, 16(4), 101333.","journal-title":"Journal of Informetrics"},{"key":"5294_CR28","doi-asserted-by":"crossref","unstructured":"Li, S., Zhao, W. X., Yin, E. J., & Wen, J. R. (2019). A Neural Citation Count Prediction Model based on Peer Review Text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 4914\u20134924). ACL.","DOI":"10.18653\/v1\/D19-1497"},{"key":"5294_CR29","unstructured":"Livne, A., Adar, E., Teevan, J., & Dumais, S. (2013). Predicting citation counts using text and graph mining. In Proceedings of the iConference 2013 workshop on computational scientometrics: Theory and applications (pp. 16\u201331). ACM."},{"issue":"8","key":"5294_CR30","doi-asserted-by":"publisher","first-page":"6803","DOI":"10.1007\/s11192-021-04033-7","volume":"126","author":"A Ma","year":"2021","unstructured":"Ma, A., Liu, Y., Xu, X., & Dong, T. (2021). A deep-learning based citation count prediction model with paper metadata semantic features. Scientometrics, 126(8), 6803\u20136823.","journal-title":"Scientometrics"},{"issue":"3","key":"5294_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102945","volume":"59","author":"Y Ma","year":"2022","unstructured":"Ma, Y., Li, T., Mao, J., Ba, Z., & Li, G. (2022). Identifying widely disseminated scientific papers on social media. Information Processing & Management, 59(3), 102945.","journal-title":"Information Processing & Management"},{"key":"5294_CR32","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., & Skiena, S. (2014). Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 701\u2013710). ACM.","DOI":"10.1145\/2623330.2623732"},{"issue":"11","key":"5294_CR33","doi-asserted-by":"publisher","first-page":"32245","DOI":"10.1007\/s11042-023-16957-8","volume":"83","author":"P Porwal","year":"2024","unstructured":"Porwal, P., & Devare, M. H. (2024). Citation count prediction using weighted latent semantic analysis (wlsa) and three-layer-deep-learning paradigm: A meta-heuristic approach. Multimedia Tools and Applications, 83(11), 32245\u201332276.","journal-title":"Multimedia Tools and Applications"},{"key":"5294_CR34","doi-asserted-by":"crossref","unstructured":"Pretorius, L., Benade, S. J., & Kruger, S. (2008). Technology forecasting: The case of Computational Fluid Dynamics (CFD). In Proceedings of the 2008 4th IEEE International Conference on Management of Innovation and Technology (pp. 7\u201311). IEEE.","DOI":"10.1109\/ICMIT.2008.4654328"},{"issue":"11","key":"5294_CR35","doi-asserted-by":"publisher","first-page":"9199","DOI":"10.1007\/s11192-021-04161-0","volume":"126","author":"T Qiu","year":"2021","unstructured":"Qiu, T., Yu, C., Zhong, Y., An, L., & Li, G. (2021). A scientific citation recommendation model integrating network and text representations. Scientometrics, 126(11), 9199\u20139221.","journal-title":"Scientometrics"},{"key":"5294_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.121071","volume":"172","author":"X Ruan","year":"2021","unstructured":"Ruan, X., Lyu, D., Gong, K., Cheng, Y., & Li, J. (2021). Rethinking the disruption index as a measure of scientific and technological advances. Technological Forecasting and Social Change, 172, 121071.","journal-title":"Technological Forecasting and Social Change"},{"key":"5294_CR37","doi-asserted-by":"crossref","unstructured":"Ruan, L., Bai, Y., Li, S., He, S., & Xiao, L. (2023). Workload time series prediction in storage systems: a deep learning based approach. Cluster Computing, pp 1\u201311.","DOI":"10.1007\/s10586-020-03214-y"},{"key":"5294_CR38","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., & Welling, M. (2018). Modeling relational data with graph convolutional networks. In Proceedings of the semantic web: 15th international conference (pp. 593\u2013607). Springer International Publishing.","DOI":"10.1007\/978-3-319-93417-4_38"},{"issue":"2","key":"5294_CR39","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1108\/JKM-01-2020-0079","volume":"25","author":"V Smojver","year":"2021","unstructured":"Smojver, V., \u0160torga, M., & Zovak, G. (2021). Exploring knowledge flow within a technology domain by conducting a dynamic analysis of a patent co-citation network. Journal of Knowledge Management, 25(2), 433\u2013453.","journal-title":"Journal of Knowledge Management"},{"key":"5294_CR40","unstructured":"Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015). Highway networks. arXiv preprint arXiv:1505.00387."},{"key":"5294_CR41","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1007\/s11192-018-2836-1","volume":"116","author":"X Sun","year":"2018","unstructured":"Sun, X., & Ding, K. (2018). Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents. Scientometrics, 116, 1735\u20131748.","journal-title":"Scientometrics"},{"key":"5294_CR42","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. In Proceeding of the advances in neural information processing systems (pp. 5998\u20136008). MIT Press."},{"issue":"6154","key":"5294_CR43","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1126\/science.1237825","volume":"342","author":"D Wang","year":"2013","unstructured":"Wang, D., Song, C., & Barab\u00e1si, A. L. (2013). Quantifying long-term scientific impact. Science, 342(6154), 127\u2013132.","journal-title":"Science"},{"issue":"1","key":"5294_CR44","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1007\/s11192-022-04541-0","volume":"128","author":"B Wang","year":"2023","unstructured":"Wang, B., Wu, F., & Shi, L. (2023). AGSTA-NET: Adaptive graph spatiotemporal attention network for citation count prediction. Scientometrics, 128(1), 511\u2013541.","journal-title":"Scientometrics"},{"issue":"8","key":"5294_CR45","doi-asserted-by":"publisher","first-page":"4315","DOI":"10.1007\/s11192-022-04462-y","volume":"127","author":"W Wei","year":"2022","unstructured":"Wei, W., Liu, H., & Sun, Z. (2022). Cover papers of top journals are reliable source for emerging topics detection: A machine learning based prediction framework. Scientometrics, 127(8), 4315\u20134333.","journal-title":"Scientometrics"},{"key":"5294_CR46","doi-asserted-by":"crossref","unstructured":"Wen, J., Wu, L., & Chai, J. (2020). Paper citation count prediction based on recurrent neural network with gated recurrent unit. In: Proceedings of the 10th International Conference on Electronics Information and Emergency Communication (ICEIEC) (pp. 303\u2013306). IEEE.","DOI":"10.1109\/ICEIEC49280.2020.9152330"},{"key":"5294_CR47","doi-asserted-by":"crossref","unstructured":"Wu, Y., Liu, X., Feng, Y., Wang, Z., Yan, R., & Zhao, D. (2019). Relation-aware entity alignment for heterogeneous knowledge graphs. arXiv preprint arXiv:1908.08210.","DOI":"10.24963\/ijcai.2019\/733"},{"key":"5294_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107839","volume":"238","author":"X Xu","year":"2022","unstructured":"Xu, X., Zhong, T., Li, C., Trajcevski, G., & Zhou, F. (2022). Heterogeneous dynamical academic network for learning scientific impact propagation. Knowledge-Based Systems, 238, 107839.","journal-title":"Knowledge-Based Systems"},{"key":"5294_CR49","doi-asserted-by":"crossref","unstructured":"Ye, B. L., Zhang, M., Li, L., Liu, C., & Wu, W. (2024). A survey of traffic flow prediction methods based on long short-term memory networks. IEEE Intelligent Transportation Systems Magazine, 16(5), 87\u2013112.","DOI":"10.1109\/MITS.2024.3400679"},{"key":"5294_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122031","volume":"238","author":"MJ Yin","year":"2024","unstructured":"Yin, M. J., Wang, B., & Ling, C. (2024). A fast local citation recommendation algorithm scalable to multi-topics. Expert Systems with Applications, 238, 122031.","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"5294_CR51","doi-asserted-by":"publisher","first-page":"1233","DOI":"10.1007\/s11192-014-1279-6","volume":"101","author":"T Yu","year":"2014","unstructured":"Yu, T., Yu, G., Li, P.-Y., & Wang, L. (2014). Citation impact prediction for scientific papers using stepwise regression analysis. Scientometrics, 101(2), 1233\u20131252.","journal-title":"Scientometrics"},{"issue":"1","key":"5294_CR52","doi-asserted-by":"publisher","DOI":"10.1016\/j.joi.2021.101235","volume":"16","author":"Q Zhao","year":"2022","unstructured":"Zhao, Q., & Feng, X. (2022). Utilizing citation network structure to predict paper citation counts: A deep learning approach. Journal of Informetrics, 16(1), 101235.","journal-title":"Journal of Informetrics"},{"key":"5294_CR53","doi-asserted-by":"crossref","unstructured":"Zhu, X. P., & Ban, Z. (2018). Citation count prediction based on academic network features. In Proceedings of the 32nd International Conference on Advanced Information Networking and Applications (AINA) (pp. 534\u2013541). IEEE","DOI":"10.1109\/AINA.2018.00084"},{"key":"5294_CR54","doi-asserted-by":"crossref","unstructured":"Zong, C., Zhuang, Y., Shao, J., & Lu, W. (2023). Citation prediction via influence representation using temporal graphs. In Proceedings of the Big Data and Social Computing (pp. 221\u2013237). Springer Nature.","DOI":"10.1007\/978-981-99-3925-1_14"}],"container-title":["Scientometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-025-05294-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11192-025-05294-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-025-05294-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T08:56:45Z","timestamp":1745917005000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11192-025-05294-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,4]]}},"alternative-id":["5294"],"URL":"https:\/\/doi.org\/10.1007\/s11192-025-05294-2","relation":{},"ISSN":["0138-9130","1588-2861"],"issn-type":[{"type":"print","value":"0138-9130"},{"type":"electronic","value":"1588-2861"}],"subject":[],"published":{"date-parts":[[2025,4]]},"assertion":[{"value":"3 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 April 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}