{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T07:17:05Z","timestamp":1782890225509,"version":"3.54.5"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"34","license":[{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T00:00:00Z","timestamp":1696291200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001793","name":"Queensland University of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100001793","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Attributed graph clustering, the task of grouping nodes into communities using both graph structure and node attributes, is a fundamental problem in graph analysis. Recent approaches have utilized deep learning for node embedding followed by conventional clustering methods. However, these methods often suffer from the limitations of relying on the original network structure, which may be inadequate for clustering due to sparsity and noise, and using separate approaches that yield suboptimal embeddings for clustering. To address these limitations, we propose a novel method called Deep Attributed Clustering with High-order Proximity Preserve (DAC-HPP) for attributed graph clustering. DAC-HPP leverages an end-to-end deep clustering framework that integrates high-order proximities and fosters structural cohesiveness and attribute homogeneity. We introduce a modified Random Walk with Restart that captures k-order structural and attribute information, enabling the modelling of interactions between network structure and high-order proximities. A consensus matrix representation is constructed by combining diverse proximity measures, and a deep joint clustering approach is employed to leverage the complementary strengths of embedding and clustering. In summary, DAC-HPP offers a unique solution for attributed graph clustering by incorporating high-order proximities and employing an end-to-end deep clustering framework. Extensive experiments demonstrate its effectiveness, showcasing its superiority over existing methods. Evaluation on synthetic and real networks demonstrates that DAC-HPP outperforms seven state-of-the-art approaches, confirming its potential for advancing attributed graph clustering research.<\/jats:p>","DOI":"10.1007\/s00521-023-09052-4","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T09:02:49Z","timestamp":1696323769000},"page":"24493-24511","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["DAC-HPP: deep attributed clustering with high-order proximity preserve"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4459-0703","authenticated-orcid":false,"given":"Kamal","family":"Berahmand","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuefeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,10,3]]},"reference":[{"issue":"2","key":"9052_CR1","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1137\/S003614450342480","volume":"45","author":"ME Newman","year":"2003","unstructured":"Newman ME (2003) The structure and function of complex networks. SIAM Rev 45(2):167\u2013256","journal-title":"SIAM Rev"},{"key":"9052_CR2","doi-asserted-by":"crossref","unstructured":"Su X, Xue S, Liu F, Wu J, Yang J, Zhou C, Hu W, Paris C, Nepal S, Jin D, et al (2022) A comprehensive survey on community detection with deep learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2021.3137396"},{"issue":"1","key":"9052_CR3","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10115-021-01623-y","volume":"64","author":"S Sieranoja","year":"2022","unstructured":"Sieranoja S, Fr\u00e4nti P (2022) Adapting k-means for graph clustering. Knowl Inf Syst 64(1):115\u2013142","journal-title":"Knowl Inf Syst"},{"issue":"4","key":"9052_CR4","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1109\/TCSS.2018.2879494","volume":"5","author":"K Berahmand","year":"2018","unstructured":"Berahmand K, Bouyer A, Vasighi M (2018) Community detection in complex networks by detecting and expanding core nodes through extended local similarity of nodes. IEEE Trans Comput Soc Syst 5(4):1021\u20131033","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"9052_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100286","volume":"37","author":"P Chunaev","year":"2020","unstructured":"Chunaev P (2020) Community detection in node-attributed social networks: a survey. Comput Sci Rev 37:100286","journal-title":"Comput Sci Rev"},{"key":"9052_CR6","doi-asserted-by":"crossref","unstructured":"Liu L, Chen P, Luo G, Kang Z, Luo Y, Han S (2022) Scalable multi-view clustering with graph filtering. Neural Comput Appl 1\u20139","DOI":"10.1007\/s00521-022-07326-x"},{"key":"9052_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108230","volume":"122","author":"C Wang","year":"2022","unstructured":"Wang C, Pan S, Celina PY, Hu R, Long G, Zhang C (2022) Deep neighbor-aware embedding for node clustering in attributed graphs. Pattern Recogn 122:108230","journal-title":"Pattern Recogn"},{"key":"9052_CR8","unstructured":"Berahmand K, Haghani S, Rostami M, Li Y (2020) A new attributed graph clustering by using label propagation in complex networks. J King Saud Univ Comput Inf Sci"},{"key":"9052_CR9","doi-asserted-by":"crossref","unstructured":"Xu W, Liu X, Gong Y (2003) Document clustering based on non-negative matrix factorization. In: Proceedings of the 26th annual international ACM SIGIR conference on research and development in information retrieval, pp. 267\u2013273","DOI":"10.1145\/860435.860485"},{"key":"9052_CR10","doi-asserted-by":"crossref","unstructured":"He C, Fei X, Cheng Q, Li H, Hu Z, Tang Y (2021) A survey of community detection in complex networks using nonnegative matrix factorization. IEEE Trans Comput Soc Syst","DOI":"10.1109\/TCSS.2021.3114419"},{"key":"9052_CR11","doi-asserted-by":"crossref","unstructured":"Golzari\u00a0Oskouei A, Balafar MA, Motamed C (2022) Edcwrn: efficient deep clustering with the weight of representations and the help of neighbors. Appl Intell 1\u201323","DOI":"10.1007\/s10489-022-03895-5"},{"key":"9052_CR12","unstructured":"Chen M-S, Lin J-Q, Li X-L, Liu B-Y, Wang C-D, Huang D, Lai J-H (2022) Representation learning in multi-view clustering: a literature review. Data Sci Eng 1\u201317"},{"key":"9052_CR13","doi-asserted-by":"publisher","first-page":"39501","DOI":"10.1109\/ACCESS.2018.2855437","volume":"6","author":"E Min","year":"2018","unstructured":"Min E, Guo X, Liu Q, Zhang G, Cui J, Long J (2018) A survey of clustering with deep learning: from the perspective of network architecture. IEEE Access 6:39501\u201339514","journal-title":"IEEE Access"},{"key":"9052_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108230","volume":"122","author":"C Wang","year":"2022","unstructured":"Wang C, Pan S, Celina PY, Hu R, Long G, Zhang C (2022) Deep neighbor-aware embedding for node clustering in attributed graphs. Pattern Recogn 122:108230","journal-title":"Pattern Recogn"},{"key":"9052_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.neunet.2021.05.008","volume":"142","author":"H Xu","year":"2021","unstructured":"Xu H, Xia W, Gao Q, Han J, Gao X (2021) Graph embedding clustering: graph attention auto-encoder with cluster-specificity distribution. Neural Netw 142:221\u2013230","journal-title":"Neural Netw"},{"issue":"3","key":"9052_CR16","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1017\/nws.2015.9","volume":"3","author":"C Bothorel","year":"2015","unstructured":"Bothorel C, Cruz JD, Magnani M, Micenkova B (2015) Clustering attributed graphs: models, measures and methods. Netw Sci 3(3):408\u2013444","journal-title":"Netw Sci"},{"key":"9052_CR17","doi-asserted-by":"crossref","unstructured":"Rostami M, Oussalah M, Berahmand K, Farrahi V (2023) Community detection algorithms in healthcare applications: a systematic review. IEEE Access","DOI":"10.1109\/ACCESS.2023.3260652"},{"issue":"1","key":"9052_CR18","doi-asserted-by":"publisher","first-page":"718","DOI":"10.14778\/1687627.1687709","volume":"2","author":"Y Zhou","year":"2009","unstructured":"Zhou Y, Cheng H, Yu JX (2009) Graph clustering based on structural\/attribute similarities. Proc VLDB Endow 2(1):718\u2013729","journal-title":"Proc VLDB Endow"},{"issue":"6","key":"9052_CR19","doi-asserted-by":"publisher","first-page":"471","DOI":"10.3390\/e20060471","volume":"20","author":"F Meng","year":"2018","unstructured":"Meng F, Rui X, Wang Z, Xing Y, Cao L (2018) Coupled node similarity learning for community detection in attributed networks. Entropy 20(6):471","journal-title":"Entropy"},{"issue":"1","key":"9052_CR20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"C Jia","year":"2017","unstructured":"Jia C, Li Y, Carson MB, Wang X, Yu J (2017) Node attribute-enhanced community detection in complex networks. Sci Rep 7(1):1\u201315","journal-title":"Sci Rep"},{"issue":"1","key":"9052_CR21","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1109\/TCYB.2017.2771496","volume":"49","author":"Y Li","year":"2017","unstructured":"Li Y, Jia C, Kong X, Yang L, Yu J (2017) Locally weighted fusion of structural and attribute information in graph clustering. IEEE Trans Cybern 49(1):247\u2013260","journal-title":"IEEE Trans Cybern"},{"issue":"8","key":"9052_CR22","doi-asserted-by":"publisher","first-page":"3203","DOI":"10.1007\/s00521-019-04064-5","volume":"32","author":"E Alinezhad","year":"2020","unstructured":"Alinezhad E, Teimourpour B, Sepehri MM, Kargari M (2020) Community detection in attributed networks considering both structural and attribute similarities: two mathematical programming approaches. Neural Comput Appl 32(8):3203\u20133220","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9052_CR23","doi-asserted-by":"publisher","first-page":"718","DOI":"10.14778\/1687627.1687709","volume":"2","author":"Y Zhou","year":"2009","unstructured":"Zhou Y, Cheng H, Yu JX (2009) Graph clustering based on structural\/attribute similarities. Proc VLDB Endow 2(1):718\u2013729","journal-title":"Proc VLDB Endow"},{"issue":"6755","key":"9052_CR24","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1038\/44565","volume":"401","author":"DD Lee","year":"1999","unstructured":"Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791","journal-title":"Nature"},{"key":"9052_CR25","doi-asserted-by":"crossref","unstructured":"Li Y, Sha C, Huang X, Zhang Y (2018) Community detection in attributed graphs: an embedding approach. In: Proceedings of the AAAI conference on artificial intelligence, vol. 32","DOI":"10.1609\/aaai.v32i1.11274"},{"key":"9052_CR26","doi-asserted-by":"crossref","unstructured":"Wang X, Jin D, Cao X, Yang L, Zhang W (2016) Semantic community identification in large attribute networks. In: Proceedings of the AAAI conference on artificial intelligence, vol. 30","DOI":"10.1609\/aaai.v30i1.9977"},{"issue":"2","key":"9052_CR27","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/s10115-021-01646-5","volume":"64","author":"D-D Lu","year":"2022","unstructured":"Lu D-D, Qi J, Yan J, Zhang Z-Y (2022) Community detection combining topology and attribute information. Knowl Inf Syst 64(2):537\u2013558","journal-title":"Knowl Inf Syst"},{"issue":"8","key":"9052_CR28","doi-asserted-by":"publisher","first-page":"7791","DOI":"10.1109\/TCYB.2021.3051021","volume":"52","author":"J Sun","year":"2021","unstructured":"Sun J, Zheng W, Zhang Q, Xu Z (2021) Graph neural network encoding for community detection in attribute networks. IEEE Trans Cybern 52(8):7791\u20137804","journal-title":"IEEE Trans Cybern"},{"key":"9052_CR29","doi-asserted-by":"crossref","unstructured":"He C, Zheng Y, Fei X, Li H, Hu Z, Tang Y (2021) Boosting nonnegative matrix factorization based community detection with graph attention auto-encoder. IEEE Trans Big Data","DOI":"10.1109\/TBDATA.2021.3103213"},{"issue":"1","key":"9052_CR30","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1007\/s10618-021-00796-y","volume":"36","author":"B Lafabregue","year":"2022","unstructured":"Lafabregue B, Weber J, Gan\u00e7arski P, Forestier G (2022) End-to-end deep representation learning for time series clustering: a comparative study. Data Min Knowl Disc 36(1):29\u201381","journal-title":"Data Min Knowl Disc"},{"key":"9052_CR31","doi-asserted-by":"crossref","unstructured":"Jin D, Yu Z, Jiao P, Pan S, He D, Wu J, Yu P, Zhang W (2021) A survey of community detection approaches: from statistical modeling to deep learning. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2021.3104155"},{"key":"9052_CR32","doi-asserted-by":"crossref","unstructured":"Su X, Xue S, Liu F, Wu J, Yang J, Zhou C, Hu W, Paris C, Nepal S, Jin D, et al (2022) A comprehensive survey on community detection with deep learning. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2021.3137396"},{"issue":"6","key":"9052_CR33","doi-asserted-by":"publisher","first-page":"2475","DOI":"10.1109\/TCYB.2019.2932096","volume":"50","author":"S Pan","year":"2019","unstructured":"Pan S, Hu R, Fung S-F, Long G, Jiang J, Zhang C (2019) Learning graph embedding with adversarial training methods. IEEE Trans Cybern 50(6):2475\u20132487","journal-title":"IEEE Trans Cybern"},{"key":"9052_CR34","doi-asserted-by":"crossref","unstructured":"Zhang Z, Yang H, Bu J, Zhou S, Yu P, Zhang J, Ester M, Wang C (2018) Anrl: attributed network representation learning via deep neural networks. In: Ijcai 18:3155\u20133161","DOI":"10.24963\/ijcai.2018\/438"},{"issue":"3","key":"9052_CR35","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1109\/TSMC.2019.2897152","volume":"51","author":"R Hong","year":"2019","unstructured":"Hong R, He Y, Wu L, Ge Y, Wu X (2019) Deep attributed network embedding by preserving structure and attribute information. IEEE Trans Syst Man Cybern Syst 51(3):1434\u20131445","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"9052_CR36","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.neunet.2021.05.008","volume":"142","author":"H Xu","year":"2021","unstructured":"Xu H, Xia W, Gao Q, Han J, Gao X (2021) Graph embedding clustering: graph attention auto-encoder with cluster-specificity distribution. Neural Netw 142:221\u2013230","journal-title":"Neural Netw"},{"key":"9052_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108230","volume":"122","author":"C Wang","year":"2022","unstructured":"Wang C, Pan S, Celina PY, Hu R, Long G, Zhang C (2022) Deep neighbor-aware embedding for node clustering in attributed graphs. Pattern Recogn 122:108230","journal-title":"Pattern Recogn"},{"key":"9052_CR38","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1016\/j.neunet.2021.05.026","volume":"142","author":"X Zhang","year":"2021","unstructured":"Zhang X, Liu H, Wu X-M, Zhang X, Liu X (2021) Spectral embedding network for attributed graph clustering. Neural Netw 142:388\u2013396","journal-title":"Neural Netw"},{"issue":"9","key":"9052_CR39","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2018","unstructured":"Cai H, Zheng VW, Chang KC-C (2018) A comprehensive survey of graph embedding: problems, techniques, and applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"9052_CR40","doi-asserted-by":"crossref","unstructured":"Pan J-Y, Yang H-J, Faloutsos C, Duygulu P (2004) Automatic multimedia cross-modal correlation discovery. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp. 653\u2013658","DOI":"10.1145\/1014052.1014135"},{"issue":"3","key":"9052_CR41","doi-asserted-by":"publisher","first-page":"0213857","DOI":"10.1371\/journal.pone.0213857","volume":"14","author":"W Jin","year":"2019","unstructured":"Jin W, Jung J, Kang U (2019) Supervised and extended restart in random walks for ranking and link prediction in networks. PLoS ONE 14(3):0213857","journal-title":"PLoS ONE"},{"issue":"2","key":"9052_CR42","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1109\/TETCI.2019.2952908","volume":"4","author":"F Xia","year":"2019","unstructured":"Xia F, Liu J, Nie H, Fu Y, Wan L, Kong X (2019) Random walks: a review of algorithms and applications. IEEE Trans Emerging Top Comput Intell 4(2):95\u2013107","journal-title":"IEEE Trans Emerging Top Comput Intell"},{"issue":"11","key":"9052_CR43","first-page":"2134","volume":"30","author":"D Zhu","year":"2018","unstructured":"Zhu D, Cui P, Zhang Z, Pei J, Zhu W (2018) High-order proximity preserved embedding for dynamic networks. IEEE Trans Knowl Data Eng 30(11):2134\u20132144","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"9052_CR44","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1089\/big.2019.0169","volume":"8","author":"M Saebi","year":"2020","unstructured":"Saebi M, Ciampaglia GL, Kaplan LM, Chawla NV (2020) Honem: learning embedding for higher order networks. Big Data 8(4):255\u2013269","journal-title":"Big Data"},{"key":"9052_CR45","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1016\/j.ins.2021.01.012","volume":"558","author":"X Sun","year":"2021","unstructured":"Sun X, Yu Y, Liang Y, Dong J, Plant C, B\u00f6hm C (2021) Fusing attributed and topological global-relations for network embedding. Inf Sci 558:76\u201390","journal-title":"Inf Sci"},{"key":"9052_CR46","unstructured":"Socher R, Pennington J, Huang EH, Ng AY, Manning CD (2011) Semi-supervised recursive autoencoders for predicting sentiment distributions. In: Proceedings of the 2011 conference on empirical methods in natural language processing, pp. 151\u2013161"},{"key":"9052_CR47","doi-asserted-by":"crossref","unstructured":"Bo D, Wang X, Shi C, Zhu M, Lu E, Cui P (2020) Structural deep clustering network. In: Proceedings of the web conference, pp. 1400\u20131410","DOI":"10.1145\/3366423.3380214"},{"key":"9052_CR48","unstructured":"Xie J, Girshick R, Farhadi A (2016) Unsupervised deep embedding for clustering analysis. In: International conference on machine learning, pp. 478\u2013487. PMLR"},{"key":"9052_CR49","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.future.2017.08.028","volume":"79","author":"W Li","year":"2018","unstructured":"Li W, Jiang S, Jin Q (2018) Overlap community detection using spectral algorithm based on node convergence degree. Futur Gener Comput Syst 79:408\u2013416","journal-title":"Futur Gener Comput Syst"},{"key":"9052_CR50","doi-asserted-by":"crossref","unstructured":"Zhou Y, Cheng H, Yu JX (2010) Clustering large attributed graphs: an efficient incremental approach. In: 2010 IEEE International conference on data mining, pp. 689\u2013698. IEEE","DOI":"10.1109\/ICDM.2010.41"},{"key":"9052_CR51","doi-asserted-by":"crossref","unstructured":"Elhadi H, Agam G (2013) Structure and attributes community detection: comparative analysis of composite, ensemble and selection methods. In: Proceedings of the 7th workshop on social network mining and analysis, pp. 1\u20137","DOI":"10.1145\/2501025.2501034"},{"key":"9052_CR52","doi-asserted-by":"crossref","unstructured":"Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Phys Rev E 78(4): 046110","DOI":"10.1103\/PhysRevE.78.046110"},{"issue":"3","key":"9052_CR53","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93\u201393","journal-title":"AI Mag"},{"key":"9052_CR54","first-page":"583","volume":"3","author":"A Strehl","year":"2002","unstructured":"Strehl A, Ghosh J (2002) Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583\u2013617","journal-title":"J Mach Learn Res"},{"key":"9052_CR55","doi-asserted-by":"crossref","unstructured":"Rand WM (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846\u2013850","DOI":"10.1080\/01621459.1971.10482356"},{"key":"9052_CR56","doi-asserted-by":"crossref","unstructured":"Chen M, Kuzmin K, Szymanski BK (2014) Community detection via maximization of modularity and its variants. IEEE Trans Comput Soc Syst 1(1):46\u201365","DOI":"10.1109\/TCSS.2014.2307458"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09052-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09052-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09052-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T13:11:06Z","timestamp":1699017066000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09052-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,3]]},"references-count":56,"journal-issue":{"issue":"34","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["9052"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09052-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,3]]},"assertion":[{"value":"20 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 October 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared no potential conflicts of interest with respect to the research, authorship, and\/or publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}