{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T21:39:12Z","timestamp":1770154752710,"version":"3.49.0"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T00:00:00Z","timestamp":1725840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Postgraduate Scientific Research Innovation Project of Hunan Province","award":["CX20220042"],"award-info":[{"award-number":["CX20220042"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Dynamic node classification aims to predict the labels of nodes in the dynamic networks. Existing methods primarily utilize the graph neural networks to acquire the node features and original graph structure features. However, these approaches ignore the high-order relationships between nodes and may lead to the over-smoothing issue. To address these issues, we propose a distance enhanced hypergraph learning (DEHL) method for dynamic node classification. Specifically, we first propose a time-adaptive pre-training component to generate the time-aware representations of each node. Then we utilize a dual-channel convolution module to construct the local and global hypergraphs which contain the corresponding local and global high-order relationships. Moreover, we adopt the K-nearest neighbor algorithm to construct the global hypergraph in the embedding space. After that, we adopt the node convolution and hyperedge convolution to aggregate the features of neighbors on the hypergraphs to the target node. Finally, we combine the temporal representations and the distance enhanced representations of the target node to predict its label. In addition, we conduct extensive experiments on two public dynamic graph datasets, i.e., Wikipedia and Reddit. The experimental results show that DEHL outperforms the state-of-the-art baselines in terms of AUC.<\/jats:p>","DOI":"10.1007\/s11063-024-11645-6","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T10:03:08Z","timestamp":1725876188000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Distance Enhanced Hypergraph Learning for Dynamic Node Classification"],"prefix":"10.1007","volume":"56","author":[{"given":"Dengfeng","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqiang","family":"Pan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengze","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,9,9]]},"reference":[{"key":"11645_CR1","doi-asserted-by":"publisher","unstructured":"Ma Y, Guo Z, Ren Z, Tang J, Yin D (2020) Streaming graph neural networks. In: Proceedings of the 43rd ACM international SIGIR conference on research and development in information retrieval (SIGIR), pp 719\u2013728. https:\/\/doi.org\/10.1145\/3397271.3401092","DOI":"10.1145\/3397271.3401092"},{"issue":"1","key":"11645_CR2","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/tnnls.2020.2978386","volume":"32","author":"Z Wu","year":"2020","unstructured":"Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4\u201324. https:\/\/doi.org\/10.1109\/tnnls.2020.2978386","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"11645_CR3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.physrep.2012.03.001","volume":"519","author":"P Holme","year":"2012","unstructured":"Holme P, Saram\u00e4ki J (2012) Temporal networks. Phys Rep 519(3):97\u2013125. https:\/\/doi.org\/10.1016\/j.physrep.2012.03.001","journal-title":"Phys Rep"},{"key":"11645_CR4","doi-asserted-by":"publisher","unstructured":"Yang C, Wang C, Lu Y, Gong X, Shi C, Wang W, Zhang X (2022) Few-shot link prediction in dynamic networks. In: Proceedings of the 15th ACM international conference on web search and data mining (WSDM), pp 1245\u20131255. https:\/\/doi.org\/10.1145\/3488560.3498417","DOI":"10.1145\/3488560.3498417"},{"key":"11645_CR5","doi-asserted-by":"publisher","unstructured":"Wen Z, Fang Y, Liu Z (2021) Meta-inductive node classification across graphs. In: Proceedings of the 44th ACM international SIGIR conference on research and development in information retrieval (SIGIR), pp 1219\u20131228. https:\/\/doi.org\/10.1145\/3404835.3462915","DOI":"10.1145\/3404835.3462915"},{"key":"11645_CR6","first-page":"2648","volume":"21","author":"SM Kazemi","year":"2020","unstructured":"Kazemi SM, Goel R, Jain K, Kobyzev I, Sethi A, Forsyth P, Poupart P (2020) Representation learning for dynamic graphs: a survey. J Mach Learn Res 21:2648\u20132720","journal-title":"J Mach Learn Res"},{"key":"11645_CR7","doi-asserted-by":"publisher","unstructured":"Khoshraftar S, An A (2022) A survey on graph representation learning methods. CoRR https:\/\/doi.org\/10.48550\/arXiv.2204.01855","DOI":"10.48550\/arXiv.2204.01855"},{"key":"11645_CR8","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"issue":"1","key":"11645_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3483595","volume":"55","author":"CD Barros","year":"2021","unstructured":"Barros CD, Mendon\u00e7a MR, Vieira AB, Ziviani A (2021) A survey on embedding dynamic graphs. ACM Comput Surv 55(1):1\u201337. https:\/\/doi.org\/10.1145\/3483595","journal-title":"ACM Comput Surv"},{"issue":"9","key":"11645_CR10","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. https:\/\/doi.org\/10.1109\/tkde.2018.2807452","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"11645_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3571808","volume":"55","author":"\u00dcV \u00c7ataly\u00fcrek","year":"2023","unstructured":"\u00c7ataly\u00fcrek \u00dcV, Devine KD, Faraj MF, Gottesb\u00fcren L, Heuer T, Meyerhenke H, Sanders P, Schlag S, Schulz C, Seemaier D, Wagner D (2023) More recent advances in (hyper) graph partitioning. ACM Comput Surv 55(12):1\u201338. https:\/\/doi.org\/10.1145\/3571808","journal-title":"ACM Comput Surv"},{"key":"11645_CR12","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.1109\/tpami.2022.3182052","volume":"45","author":"Y Gao","year":"2023","unstructured":"Gao Y, Feng Y, Ji S, Ji R (2023) Hgnn$$ ^{\\text{+ }}$$: general hypergraph neural networks. IEEE Trans Pattern Anal Mach Intell 45:3181\u20133199. https:\/\/doi.org\/10.1109\/tpami.2022.3182052","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11645_CR13","doi-asserted-by":"publisher","unstructured":"Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32th AAAI conference on artificial intelligence (AAAI), vol 32 . https:\/\/doi.org\/10.1609\/aaai.v32i1.11604","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"11645_CR14","unstructured":"Xu K, Li C, Tian Y, Sonobe T, Kawarabayashi K-i, Jegelka S (2018) Representation learning on graphs with jumping knowledge networks. In: Proceedings of the 35th international conference on machine learning (ICML), vol. 80, pp 5453\u20135462"},{"key":"11645_CR15","doi-asserted-by":"publisher","unstructured":"Chen J, Ma T, Xiao C (2018) Fastgcn: fast learning with graph convolutional networks via importance sampling. In: Proceedings of the 6th international conference on learning representations (ICLR). https:\/\/doi.org\/10.48550\/arXiv.1801.10247","DOI":"10.48550\/arXiv.1801.10247"},{"key":"11645_CR16","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th international conference on learning representations (ICLR)"},{"key":"11645_CR17","doi-asserted-by":"publisher","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Proceedings of the 31th conference on neural information processing systems (NeurIPS), pp 1024\u20131034 . https:\/\/doi.org\/10.48550\/arXiv.1706.02216","DOI":"10.48550\/arXiv.1706.02216"},{"key":"11645_CR18","unstructured":"Velickovic P, Cucurull G, Casanova A, et al (2018) Graph attention networks. In: Proceedings of the 6th international conference on learning representations (ICLR)"},{"key":"11645_CR19","unstructured":"Li Y, Tarlow D, Brockschmidt M, Zemel RS (2016) Gated graph sequence neural networks. In: Proceedings of the 4th international conference on learning representations (ICLR)"},{"key":"11645_CR20","doi-asserted-by":"publisher","unstructured":"Li Q, Zhang X, Liu H, Dai Q, Wu X-M (2021) Dimensionwise separable 2-d graph convolution for unsupervised and semi-supervised learning on graphs. In: Proceedings of the 27th International conference on knowledge discovery and data mining (KDD), pp 953\u2013963. https:\/\/doi.org\/10.1145\/3447548.3467413","DOI":"10.1145\/3447548.3467413"},{"issue":"10","key":"11645_CR21","doi-asserted-by":"publisher","first-page":"2765","DOI":"10.1109\/tkde.2016.2591009","volume":"28","author":"L Zhu","year":"2016","unstructured":"Zhu L, Guo D, Yin J, Ver Steeg G, Galstyan A (2016) Scalable temporal latent space inference for link prediction in dynamic social networks. IEEE Trans Knowl Data Eng 28(10):2765\u20132777. https:\/\/doi.org\/10.1109\/tkde.2016.2591009","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11645_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.06.024","volume":"187","author":"P Goyal","year":"2020","unstructured":"Goyal P, Chhetri SR, Canedo A (2020) dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowl-Based Syst 187:104816. https:\/\/doi.org\/10.1016\/j.knosys.2019.06.024","journal-title":"Knowl-Based Syst"},{"key":"11645_CR23","doi-asserted-by":"publisher","unstructured":"Pareja A, Domeniconi G, Chen J, Ma T, Suzumura T, Kanezashi H, Kaler T, Schardl T, Leiserson C (2020) Evolvegcn: evolving graph convolutional networks for dynamic graphs. In: Proceedings of the 34th AAAI conference on artificial intelligence (AAAI), vol 34, pp 5363\u20135370. https:\/\/doi.org\/10.1109\/tkde.2016.2591009","DOI":"10.1109\/tkde.2016.2591009"},{"key":"11645_CR24","unstructured":"Goyal P, Kamra N, He X, Liu Y (2018) Dyngem: deep embedding method for dynamic graphs. CoRR arxiv:1805.11273"},{"key":"11645_CR25","doi-asserted-by":"publisher","unstructured":"Zhou L, Yang Y, Ren X, Wu F, Zhuang Y (2018) Dynamic network embedding by modeling triadic closure process. In: Proceedings of the 32th AAAI conference on artificial intelligence (AAAI), vol 32. https:\/\/doi.org\/10.1609\/aaai.v32i1.11257","DOI":"10.1609\/aaai.v32i1.11257"},{"key":"11645_CR26","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Cui, P., Pei, J., Wang, X., Zhu, W.: Timers: Error-bounded svd restart on dynamic networks. In: Proceedings of the 32th AAAI conference on artificial intelligence (AAAI), vol 32 (2018). https:\/\/doi.org\/10.1609\/aaai.v32i1.11299","DOI":"10.1609\/aaai.v32i1.11299"},{"key":"11645_CR27","unstructured":"Trivedi R, Farajtabar M, Biswal P, Zha H (2019) Dyrep: Learning representations over dynamic graphs. In: Proceedings of the 7th international conference on learning representations (ICLR)"},{"key":"11645_CR28","unstructured":"Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K (2020) Inductive representation learning on temporal graphs. In: Proceedings of the 8th international conference on learning representations (ICLR)"},{"key":"11645_CR29","unstructured":"Xu D, Ruan C, Korpeoglu E, Kumar S, Achan K (2019) Self-attention with functional time representation learning. In: Proceedings of the 33th conference on neural information processing systems (NeurIPS), pp 15889\u201315899"},{"key":"11645_CR30","doi-asserted-by":"publisher","unstructured":"Jiang J, Wei Y, Feng Y, Cao J, Gao Y (2019) Dynamic hypergraph neural networks. In: Proceedings of the 28th conference on international joint conference on artificial intelligence (IJCAI), pp 2635\u20132641. https:\/\/doi.org\/10.24963\/ijcai.2019\/366","DOI":"10.24963\/ijcai.2019\/366"},{"key":"11645_CR31","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2016.07.030","volume":"216","author":"Y Zhu","year":"2016","unstructured":"Zhu Y, Guan Z, Tan S, Liu H, Cai D, He X (2016) Heterogeneous hypergraph embedding for document recommendation. Neurocomputing 216:150\u2013162. https:\/\/doi.org\/10.1016\/j.neucom.2016.07.030","journal-title":"Neurocomputing"},{"issue":"1","key":"11645_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2037676.2037679","volume":"7","author":"S Tan","year":"2011","unstructured":"Tan S, Bu J, Chen C, Xu B, Wang C, He X (2011) Using rich social media information for music recommendation via hypergraph model. ACM Trans Multimed Comput Commun Appl 7(1):1\u201322. https:\/\/doi.org\/10.1145\/2037676.2037679","journal-title":"ACM Trans Multimed Comput Commun Appl"},{"issue":"9","key":"11645_CR33","doi-asserted-by":"publisher","first-page":"2564","DOI":"10.1109\/tkde.2015.2415497","volume":"27","author":"M Wang","year":"2015","unstructured":"Wang M, Liu X, Wu X (2015) Visual classification by $$\\ell _1$$-hypergraph modeling. IEEE Trans Knowl Data Eng 27(9):2564\u20132574. https:\/\/doi.org\/10.1109\/tkde.2015.2415497","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9","key":"11645_CR34","doi-asserted-by":"publisher","first-page":"4290","DOI":"10.1109\/tip.2012.2199502","volume":"21","author":"Y Gao","year":"2012","unstructured":"Gao Y, Wang M, Tao D, Ji R, Dai Q (2012) 3-d object retrieval and recognition with hypergraph analysis. IEEE Trans Image Process 21(9):4290\u20134303. https:\/\/doi.org\/10.1109\/tip.2012.2199502","journal-title":"IEEE Trans Image Process"},{"key":"11645_CR35","doi-asserted-by":"publisher","unstructured":"Huang Y, Liu Q, Zhang S, Metaxas DN (2010) Image retrieval via probabilistic hypergraph ranking. In: Proceedings of the 23rd conference on computer vision and pattern recognition (CVPR), pp 3376\u20133383 . https:\/\/doi.org\/10.1109\/CVPR.2010.5540012","DOI":"10.1109\/CVPR.2010.5540012"},{"key":"11645_CR36","doi-asserted-by":"crossref","unstructured":"Zhou D, Huang J, Sch\u00f6lkopf B (2006) Learning with hypergraphs: Clustering, classification, and embedding. In: Proceedings of the 20th conference on neural information processing systems (NeurIPS), pp 1601\u20131608","DOI":"10.7551\/mitpress\/7503.003.0205"},{"key":"11645_CR37","doi-asserted-by":"publisher","unstructured":"Huang Y, Liu Q, Metaxas D (2009) Video object segmentation by hypergraph cut. In: Proceedings of the 25th conference on computer vision and pattern recognition (CVPR), pp 1738\u20131745 https:\/\/doi.org\/10.1109\/CVPR.2009.5206795","DOI":"10.1109\/CVPR.2009.5206795"},{"key":"11645_CR38","doi-asserted-by":"publisher","unstructured":"Zhang Z, Lin H, Gao Y (2018) Dynamic hypergraph structure learning. In: Proceedings of the 27th conference on international joint conference on artificial intelligence (IJCAI), pp 3162\u20133169. https:\/\/doi.org\/10.24963\/ijcai.2018\/439","DOI":"10.24963\/ijcai.2018\/439"},{"key":"11645_CR39","doi-asserted-by":"publisher","unstructured":"Dong Y, Sawin W, Bengio Y (2020) HNHN: Hypergraph networks with hyperedge neurons. CoRR https:\/\/doi.org\/10.48550\/arXiv.2006.12278","DOI":"10.48550\/arXiv.2006.12278"},{"key":"11645_CR40","doi-asserted-by":"publisher","unstructured":"Zhang Y, Wang N, Chen Y, Zou C, Wan H, Zhao X, Gao Y (2020) Hypergraph label propagation network. In: Proceedings of the 34th AAAI conference on artificial intelligence (AAAI), vol. 34, pp 6885\u20136892. https:\/\/doi.org\/10.24963\/ijcai.2018\/439","DOI":"10.24963\/ijcai.2018\/439"},{"key":"11645_CR41","unstructured":"Zhang R, Zou Y, Ma J (2020) Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. In: Proceedings of the 4th international conference on learning representations (ICLR)"},{"key":"11645_CR42","doi-asserted-by":"publisher","unstructured":"Feng Y, You H, Zhang Z, Ji R, Gao Y (2019) Hypergraph neural networks. In: Proceedings of the 33th AAAI conference on artificial intelligence (AAAI), vol 33, pp 3558\u20133565 . https:\/\/doi.org\/10.1609\/aaai.v33i01.33013558","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"11645_CR43","doi-asserted-by":"publisher","unstructured":"Yang C, Wang R, Yao S, Abdelzaher TF (2022) Semi-supervised hypergraph node classification on hypergraph line expansion. In: Proceedings of the 31st international conference on information knowledge management (CIKM), pp 2352\u20132361. https:\/\/doi.org\/10.1145\/3511808.3557447","DOI":"10.1145\/3511808.3557447"},{"key":"11645_CR44","doi-asserted-by":"publisher","unstructured":"Wang J, Ding K, Hong L, Liu H, Caverlee J (2020) Next-item recommendation with sequential hypergraphs. In: Proceedings of the 43rd international conference on research and development in information retrieval (SIGIR), pp 1101\u20131110. https:\/\/doi.org\/10.1145\/3397271.3401133","DOI":"10.1145\/3397271.3401133"},{"key":"11645_CR45","doi-asserted-by":"publisher","unstructured":"Amburg I, Veldt N, Benson AR (2020) Clustering in graphs and hypergraphs with categorical edge labels. In: Proceedings of the 20th international conference on the web conference (WWW), pp 706\u2013717 . https:\/\/doi.org\/10.1145\/3366423.3380152","DOI":"10.1145\/3366423.3380152"},{"key":"11645_CR46","unstructured":"Rossi E, Chamberlain B, Frasca F, Eynard D, Monti F, Bronstein M (2020) Temporal graph networks for deep learning on dynamic graphs. In: Proceedings of the 35th international conference on machine learning (ICML)"},{"key":"11645_CR47","unstructured":"Jin M, Li Y, Pan S (2022) Neural temporal walks: motif-aware representation learning on continuous-time dynamic graphs. In: Proceedings of the 33th conference on neural information processing systems (NeurIPS), pp 15889\u201315899"},{"issue":"11","key":"11645_CR48","doi-asserted-by":"publisher","first-page":"6737","DOI":"10.1109\/TNNLS.2021.3083318","volume":"33","author":"J Chen","year":"2022","unstructured":"Chen J, Gong Z, Mo J, Wang W, Wang W, Wang C, Dong X, Liu W, Wu K (2022) Self-training enhanced: network embedding and overlapping community detection with adversarial learning. IEEE Trans Neural Netw Learn Syst 33(11):6737\u20136748. https:\/\/doi.org\/10.1109\/TNNLS.2021.3083318","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11645_CR49","doi-asserted-by":"publisher","unstructured":"Chen J, Wang J, Dai Z, Wu H, Wang M, Zhang Q, Wang H (2023) Zero-shot micro-video classification with neural variational inference in graph prototype network. In: Proceedings of the 31st ACM international conference on multimedia (MM). https:\/\/doi.org\/10.1145\/3581783.3611740","DOI":"10.1145\/3581783.3611740"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11645-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-024-11645-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-024-11645-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T15:56:35Z","timestamp":1730303795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-024-11645-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,9]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["11645"],"URL":"https:\/\/doi.org\/10.1007\/s11063-024-11645-6","relation":{},"ISSN":["1573-773X"],"issn-type":[{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,9,9]]},"assertion":[{"value":"8 May 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"231"}}