{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T07:05:13Z","timestamp":1779692713194,"version":"3.53.1"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72021001"],"award-info":[{"award-number":["72021001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neural Networks"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.neunet.2026.108851","type":"journal-article","created":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T23:37:30Z","timestamp":1773531450000},"page":"108851","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Self-weighted low-rank representation for multivariate compositional data"],"prefix":"10.1016","volume":"200","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8397-5912","authenticated-orcid":false,"given":"Zhengyan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8907-7746","authenticated-orcid":false,"given":"Huiwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2888-4207","authenticated-orcid":false,"given":"Qing","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0179-2364","authenticated-orcid":false,"given":"Lihong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.neunet.2026.108851_bib0001","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.3390\/electronics9081295","article-title":"The k-means algorithm: A comprehensive survey and performance evaluation","volume":"9","author":"Ahmed","year":"2020","journal-title":"Electronics"},{"issue":"2","key":"10.1016\/j.neunet.2026.108851_bib0002","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","article-title":"The statistical analysis of compositional data","volume":"44","author":"Aitchison","year":"1982","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"16","key":"10.1016\/j.neunet.2026.108851_bib0003","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1080\/03610926.2021.2014890","article-title":"A review of compositional data analysis and recent advances","volume":"52","author":"Alenazi","year":"2023","journal-title":"Communications in Statistics-Theory and Methods"},{"key":"10.1016\/j.neunet.2026.108851_bib0004","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.inffus.2022.10.020","article-title":"Seeking commonness and inconsistencies: A jointly smoothed approach to multi-view subspace clustering","volume":"91","author":"Cai","year":"2023","journal-title":"Information Fusion"},{"issue":"2","key":"10.1016\/j.neunet.2026.108851_bib0005","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/TAI.2021.3065894","article-title":"A survey on multiview clustering","volume":"2","author":"Chao","year":"2021","journal-title":"IEEE Transactions on Artificial Intelligence"},{"key":"10.1016\/j.neunet.2026.108851_bib0006","doi-asserted-by":"crossref","first-page":"3713","DOI":"10.1007\/s00521-020-05227-5","article-title":"Novel multivariate compositional data\u2019s model for structurally analyzing sub-industrial energy consumption with economic data","volume":"33","author":"Chen","year":"2021","journal-title":"Neural Computing and Applications"},{"issue":"4","key":"10.1016\/j.neunet.2026.108851_bib0007","doi-asserted-by":"crossref","first-page":"994","DOI":"10.1080\/10618600.2014.948181","article-title":"Splitting methods for convex clustering","volume":"24","author":"Chi","year":"2015","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"10.1016\/j.neunet.2026.108851_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.gexplo.2022.107012","article-title":"Using multivariate compositional data analysis (coda) and clustering to establish geochemical backgrounds in stream sediments of an onshore oil deposits area. the agri river basin (italy) case study","volume":"238","author":"Cicchella","year":"2022","journal-title":"Journal of Geochemical Exploration"},{"key":"10.1016\/j.neunet.2026.108851_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.111553","article-title":"Multi-view subspace clustering based on adaptive search","volume":"289","author":"Dong","year":"2024","journal-title":"Knowledge-Based Systems"},{"issue":"3","key":"10.1016\/j.neunet.2026.108851_bib0010","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1023\/A:1023818214614","article-title":"Isometric logratio transformations for compositional data analysis","volume":"35","author":"Egozcue","year":"2003","journal-title":"Mathematical Geology"},{"issue":"11","key":"10.1016\/j.neunet.2026.108851_bib0011","doi-asserted-by":"crossref","first-page":"2765","DOI":"10.1109\/TPAMI.2013.57","article-title":"Sparse subspace clustering: Algorithm, theory, and applications","volume":"35","author":"Elhamifar","year":"2013","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"12","key":"10.1016\/j.neunet.2026.108851_bib0012","doi-asserted-by":"crossref","first-page":"12350","DOI":"10.1109\/TKDE.2023.3270311","article-title":"A comprehensive survey on multi-view clustering","volume":"35","author":"Fang","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"3","key":"10.1016\/j.neunet.2026.108851_bib0013","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s11004-023-10115-4","article-title":"Insights in hierarchical clustering of variables for compositional data","volume":"56","author":"\u0144 Fern\u00e1ndez","year":"2024","journal-title":"Mathematical Geosciences"},{"issue":"11","key":"10.1016\/j.neunet.2026.108851_bib0014","doi-asserted-by":"crossref","first-page":"6870","DOI":"10.1109\/TCYB.2022.3166545","article-title":"Latent low-rank representation with weighted distance penalty for clustering","volume":"53","author":"Fu","year":"2022","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.neunet.2026.108851_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109063","article-title":"Auto-weighted low-rank representation for clustering","volume":"251","author":"Fu","year":"2022","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"10.1016\/j.neunet.2026.108851_bib0016","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1080\/02664763.2018.1454894","article-title":"Clustering transformed compositional data using k-means, with applications in gene expression and bicycle sharing system data","volume":"46","author":"Godichon-Baggioni","year":"2019","journal-title":"Journal of Applied Statistics"},{"key":"10.1016\/j.neunet.2026.108851_bib0017","first-page":"144584","author":"Graffelman","year":"2017"},{"key":"10.1016\/j.neunet.2026.108851_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106614","article-title":"A classification framework for multivariate compositional data with dirichlet feature embedding","volume":"212","author":"Gu","year":"2021","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"10.1016\/j.neunet.2026.108851_bib0019","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1109\/TKDE.2020.2986201","article-title":"Multi-view k-means clustering with adaptive sparse memberships and weight allocation","volume":"34","author":"Han","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.108851_bib0020","doi-asserted-by":"crossref","first-page":"655","DOI":"10.1007\/s11004-020-09862-5","article-title":"Weighted symmetric pivot coordinates for compositional data with geochemical applications","volume":"53","author":"Hron","year":"2021","journal-title":"Mathematical Geosciences"},{"key":"10.1016\/j.neunet.2026.108851_bib0021","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.patcog.2018.11.007","article-title":"Auto-weighted multi-view clustering via kernelized graph learning","volume":"88","author":"Huang","year":"2019","journal-title":"Pattern Recognition"},{"issue":"1","key":"10.1016\/j.neunet.2026.108851_bib0022","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":", Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"Journal of Classification"},{"key":"10.1016\/j.neunet.2026.108851_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.109388","article-title":"Global and local structure preserving nonnegative subspace clustering","volume":"138","author":"Jia","year":"2023","journal-title":"Pattern Recognition"},{"issue":"1","key":"10.1016\/j.neunet.2026.108851_bib0024","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1007\/s10044-024-01405-6","article-title":"Self-weighted subspace clustering via adaptive rank constrained graph embedding","volume":"28","author":"Jiang","year":"2025","journal-title":"Pattern Analysis and Applications"},{"key":"10.1016\/j.neunet.2026.108851_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125831","article-title":"Robust multi-view subspace clustering via neighbor embedding on manifold and low-rank representation learning","volume":"267","author":"Kong","year":"2025","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.neunet.2026.108851_bib0026","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.neucom.2023.01.051","article-title":"Projection-preserving block-diagonal low-rank representation for subspace clustering","volume":"526","author":"Kong","year":"2023","journal-title":"Neurocomputing"},{"issue":"10","key":"10.1016\/j.neunet.2026.108851_bib0027","doi-asserted-by":"crossref","first-page":"10569","DOI":"10.1109\/TKDE.2023.3249765","article-title":"Structure-aware subspace clustering","volume":"35","author":"Kou","year":"2023","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.neunet.2026.108851_bib0028","series-title":"Multiview subspace clustering via low-rank symmetric affinity graph","author":"Lan","year":"2023"},{"key":"10.1016\/j.neunet.2026.108851_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124699","article-title":"Attributed graph subspace clustering with residual compensation guided by adaptive dual manifold regularization","volume":"255","author":"Li","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.neunet.2026.108851_bib0030","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1109\/TPAMI.2012.88","article-title":"Robust recovery of subspace structures by low-rank representation","volume":"35","author":"Liu","year":"2012","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"10.1016\/j.neunet.2026.108851_bib0031","series-title":"Self-weighted subspace clustering with adaptive neighbors","first-page":"129754","author":"Liu","year":"2025"},{"key":"10.1016\/j.neunet.2026.108851_bib0032","series-title":"12Th european conference on computer vision","first-page":"347","article-title":"Robust and efficient subspace segmentation via least squares regression","author":"Lu","year":"2012"},{"key":"10.1016\/j.neunet.2026.108851_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107023","article-title":"Md-mbpls: A novel explanatory model in computational social science","volume":"223","author":"Lu","year":"2021","journal-title":"Knowledge-Based Systems"},{"key":"10.1016\/j.neunet.2026.108851_bib0034","series-title":"Ieee transactions on circuits and systems for video technology","article-title":"Symmetric multi-view subspace clustering with automatic neighbor discovery","author":"Ma","year":"2024"},{"issue":"3","key":"10.1016\/j.neunet.2026.108851_bib0035","doi-asserted-by":"crossref","first-page":"1476","DOI":"10.1214\/21-AOAS1552","article-title":"Dirichlet-tree multinomial mixtures for clustering microbiome compositions","volume":"16","author":"Mao","year":"2022","journal-title":"The Annals of Applied Statistics"},{"issue":"3","key":"10.1016\/j.neunet.2026.108851_bib0036","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1109\/TKDE.2020.2995896","article-title":"Robust subspace clustering with low-rank structure constraint","volume":"34","author":"Nie","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"7","key":"10.1016\/j.neunet.2026.108851_bib0037","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1016\/j.watres.2005.01.012","article-title":"Relative vs. absolute statistical analysis of compositions: A comparative study of surface waters of a mediterranean river","volume":"39","author":"Otero","year":"2005","journal-title":"Water Research"},{"key":"10.1016\/j.neunet.2026.108851_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128839","article-title":"Adaptive multi-view subspace clustering algorithm based on representative features and redundant instances","volume":"620","author":"Ou","year":"2025","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.neunet.2026.108851_bib0039","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0268438","article-title":"Clustering compositional data using dirichlet mixture model","volume":"17","author":"Pal","year":"2022","journal-title":"PloS one"},{"issue":"2","key":"10.1016\/j.neunet.2026.108851_bib0040","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1007\/s00357-012-9105-4","article-title":"Dealing with distances and transformations for fuzzy c-means clustering of compositional data","volume":"29","author":"Palarea-Albaladejo","year":"2012","journal-title":"Journal of Classification"},{"key":"10.1016\/j.neunet.2026.108851_bib0041","series-title":"Modeling and analysis of compositional data","author":"Pawlowsky-Glahn","year":"2015"},{"issue":"1","key":"10.1016\/j.neunet.2026.108851_bib0042","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-72006-w","article-title":"Bayesian clustering of spatially distributed compositional data with application to the great barrier reef","volume":"14","author":"Piancastelli","year":"2024","journal-title":"Scientific Reports"},{"issue":"2","key":"10.1016\/j.neunet.2026.108851_bib0043","doi-asserted-by":"crossref","first-page":"436","DOI":"10.3390\/math11020436","article-title":"A survey on high-dimensional subspace clustering","volume":"11","author":"Qu","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.neunet.2026.108851_bib0044","first-page":"583","article-title":"Cluster ensembles-a knowledge reuse framework for combining multiple partitions","volume":"3","author":"Strehl","year":"2002","journal-title":"Journal of Machine Learning Research"},{"issue":"2","key":"10.1016\/j.neunet.2026.108851_bib0045","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1080\/10618600.2017.1377081","article-title":"Sparse convex clustering","volume":"27","author":"Wang","year":"2018","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"10.1016\/j.neunet.2026.108851_bib0046","series-title":"Handbook of partial least squares: Concepts, methods and applications","first-page":"381","article-title":"Regression modelling analysis on compositional data","author":"Wang","year":"2009"},{"key":"10.1016\/j.neunet.2026.108851_bib0047","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s00362-019-01093-z","article-title":"Sliced inverse regression method for multivariate compositional data modeling","volume":"62","author":"Wang","year":"2021","journal-title":"Statistical Papers"},{"issue":"6","key":"10.1016\/j.neunet.2026.108851_bib0048","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TKDE.2019.2903810","article-title":"Gmc: Graph-based multi-view clustering","volume":"32","author":"Wang","year":"2019","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"10.1016\/j.neunet.2026.108851_bib0049","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1109\/TFUZZ.2023.3294921","article-title":"Local-global fuzzy clustering with anchor graph","volume":"32","author":"Wang","year":"2023","journal-title":"IEEE Transactions on Fuzzy Systems"},{"key":"10.1016\/j.neunet.2026.108851_bib0050","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.neucom.2020.10.105","article-title":"Convex clustering method for compositional data via sparse group lasso","volume":"425","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neunet.2026.108851_bib0051","doi-asserted-by":"crossref","first-page":"2965","DOI":"10.1007\/s00500-020-05355-z","article-title":"Convex clustering method for compositional data modeling","volume":"25","author":"Wang","year":"2021","journal-title":"Soft Computing"},{"key":"10.1016\/j.neunet.2026.108851_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110592","article-title":"Joint learning of latent subspace and structured graph for multi-view clustering","volume":"154","author":"Wang","year":"2024","journal-title":"Pattern Recognition"},{"issue":"9","key":"10.1016\/j.neunet.2026.108851_bib0053","doi-asserted-by":"crossref","first-page":"4610","DOI":"10.1109\/TNNLS.2021.3059511","article-title":"Subspace clustering via structured sparse relation representation","volume":"33","author":"Wei","year":"2021","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10.1016\/j.neunet.2026.108851_bib0054","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.scitotenv.2018.03.325","article-title":"Promoting inclusive water governance and forecasting the structure of water consumption based on compositional data: A case study of beijing","volume":"634","author":"Wei","year":"2018","journal-title":"Science of the Total Environment"},{"issue":"3","key":"10.1016\/j.neunet.2026.108851_bib0055","doi-asserted-by":"crossref","first-page":"1493","DOI":"10.1109\/TCYB.2019.2943691","article-title":"Scaled simplex representation for subspace clustering","volume":"51","author":"Xu","year":"2019","journal-title":"IEEE Transactions on Cybernetics"},{"key":"10.1016\/j.neunet.2026.108851_bib0056","doi-asserted-by":"crossref","DOI":"10.1016\/j.coal.2021.103892","article-title":"Coal elemental (compositional) data analysis with hierarchical clustering algorithms","volume":"249","author":"Xu","year":"2022","journal-title":"International Journal of Coal Geology"},{"key":"10.1016\/j.neunet.2026.108851_bib0057","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.122765","article-title":"Two-step affinity matrix learning for multi-view subspace clustering","volume":"242","author":"Zhang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.neunet.2026.108851_bib0058","series-title":"Reweighted subspace clustering guided by local and global structure preservation","author":"Zhou","year":"2025"},{"key":"10.1016\/j.neunet.2026.108851_bib0059","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102821","article-title":"Hierarchical bipartite graph based multi-view subspace clustering","volume":"117","author":"Zhou","year":"2025","journal-title":"Information Fusion"},{"key":"10.1016\/j.neunet.2026.108851_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.129065","article-title":"Graph regularized least squares regression for automated breast ultrasound imaging","volume":"619","author":"Zhou","year":"2025","journal-title":"Neurocomputing"}],"container-title":["Neural Networks"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026003138?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0893608026003138?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T06:40:04Z","timestamp":1779691204000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0893608026003138"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":60,"alternative-id":["S0893608026003138"],"URL":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108851","relation":{},"ISSN":["0893-6080"],"issn-type":[{"value":"0893-6080","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Self-weighted low-rank representation for multivariate compositional data","name":"articletitle","label":"Article Title"},{"value":"Neural Networks","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neunet.2026.108851","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"108851"}}