{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:22:14Z","timestamp":1765610534079,"version":"3.37.3"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-03453-5","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T06:18:59Z","timestamp":1733120339000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["On Hypergraph Neural Networks and Their Stability Towards Higher-Order Knowledge Representation and Learning"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5373-8912","authenticated-orcid":false,"given":"Bikram Pratim","family":"Bhuyan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9991-6494","authenticated-orcid":false,"given":"Thipendra P.","family":"Singh","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8957-6756","authenticated-orcid":false,"given":"Ravi","family":"Tomar","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8289-747X","authenticated-orcid":false,"given":"Amar","family":"Ramdane-Cherif","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"3453_CR1","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1016\/j.neucom.2019.12.130","volume":"408","author":"Y Chong","year":"2020","unstructured":"Chong Y, Ding Y, Yan Q, Pan S. Graph-based semi-supervised learning: a review. Neurocomputing. 2020;408:216\u201330.","journal-title":"Neurocomputing"},{"key":"3453_CR2","doi-asserted-by":"crossref","unstructured":"Bhuyan BP, Karmakar A, Hazarika SM. Bounding stability in formal concept analysis. In: Advanced computational and communication paradigms: Proceedings of international conference on ICACCP 2017, vol. 2. Springer; 2018. p. 545\u201352.","DOI":"10.1007\/978-981-10-8237-5_53"},{"key":"3453_CR3","doi-asserted-by":"publisher","first-page":"1278","DOI":"10.1016\/j.procs.2024.04.121","volume":"235","author":"BP Bhuyan","year":"2024","unstructured":"Bhuyan BP, Singh TP, Tomar R, Ramdane-Cherif A. Nesykhg: neuro-symbolic knowledge hypergraphs. Procedia Comput Sci. 2024;235:1278\u201388.","journal-title":"Procedia Comput Sci"},{"key":"3453_CR4","unstructured":"Maleki S, Hajiramezanali E, Scalia G, Biancalani T, Chuang KV. Learning to explain hypergraph neural networks. In: Annual workshop on topology, algebra, and geometry in machine learning (TAG-ML). 2023. https:\/\/openreview.net\/forum?id=B6YeDatcFw"},{"key":"3453_CR5","doi-asserted-by":"crossref","unstructured":"Cai D, Song M, Sun C, Zhang B, Hong S, Li H. Hypergraph structure learning for hypergraph neural networks. In: Proceedings of the thirty-first international joint conference on artificial intelligence, IJCAI-22. (2022). p. 1923\u20139.","DOI":"10.24963\/ijcai.2022\/267"},{"key":"3453_CR6","doi-asserted-by":"crossref","unstructured":"Bhuyan BP, Tomar R, Gupta M, Ramdane-Cherif A. An ontological knowledge representation for smart agriculture. In: 2021 IEEE international conference on big data (big data). IEEE; 2021. p. 3400\u20136.","DOI":"10.1109\/BigData52589.2021.9672020"},{"issue":"22","key":"3453_CR7","doi-asserted-by":"publisher","first-page":"15249","DOI":"10.3390\/su142215249","volume":"14","author":"BP Bhuyan","year":"2022","unstructured":"Bhuyan BP, Tomar R, Cherif AR. A systematic review of knowledge representation techniques in smart agriculture (urban). Sustainability. 2022;14(22):15249.","journal-title":"Sustainability"},{"issue":"1","key":"3453_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3605776","volume":"56","author":"A Antelmi","year":"2023","unstructured":"Antelmi A, Cordasco G, Polato M, Scarano V, Spagnuolo C, Yang D. A survey on hypergraph representation learning. ACM Comput Surv. 2023;56(1):1\u201338.","journal-title":"ACM Comput Surv"},{"key":"3453_CR9","doi-asserted-by":"crossref","unstructured":"Feng Y, You H, Zhang Z, Ji R, Gao Y. Hypergraph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33. 2019. p. 3558\u201365.","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"5","key":"3453_CR10","first-page":"1","volume":"17","author":"M Li","year":"2023","unstructured":"Li M, Zhang Y, Li X, Zhang Y, Yin B. Hypergraph transformer neural networks. ACM Trans Knowl Discov Data. 2023;17(5):1\u201322.","journal-title":"ACM Trans Knowl Discov Data"},{"key":"3453_CR11","doi-asserted-by":"crossref","unstructured":"Wang M, Zhen Y, Pan Y, Zhao Y, Zhuang C, Xu Z, Guo R, Zhao X. Tensorized hypergraph neural networks. In: Proceedings of the 2024 SIAM international conference on data mining (SDM). SIAM; 2024. p. 127\u201335.","DOI":"10.1137\/1.9781611978032.15"},{"key":"3453_CR12","doi-asserted-by":"publisher","first-page":"102538","DOI":"10.1016\/j.sbi.2023.102538","volume":"79","author":"P Veli\u010dkovi\u0107","year":"2023","unstructured":"Veli\u010dkovi\u0107 P. Everything is connected: graph neural networks. Curr Opin Struct Biol. 2023;79:102538.","journal-title":"Curr Opin Struct Biol"},{"key":"3453_CR13","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. Graph neural networks: a review of methods and applications. AI Open. 2020;1:57\u201381.","journal-title":"AI Open"},{"key":"3453_CR14","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 2018."},{"key":"3453_CR15","unstructured":"Bo D, Shi C, Wang L, Liao R. Specformer: spectral graph neural networks meet transformers. arXiv preprint arXiv:2303.01028 2023."},{"key":"3453_CR16","doi-asserted-by":"publisher","first-page":"192435","DOI":"10.1109\/ACCESS.2020.3030076","volume":"8","author":"Z Chen","year":"2020","unstructured":"Chen Z, Wang Y, Zhao B, Cheng J, Zhao X, Duan Z. Knowledge graph completion: a review. IEEE Access. 2020;8:192435\u201356.","journal-title":"IEEE Access"},{"key":"3453_CR17","doi-asserted-by":"crossref","unstructured":"Naidu G, Zuva T, Sibanda EM. A review of evaluation metrics in machine learning algorithms. In: Computer science on-line conference. Springer; 2023. p. 15\u201325.","DOI":"10.1007\/978-3-031-35314-7_2"},{"issue":"3","key":"3453_CR18","doi-asserted-by":"publisher","first-page":"3181","DOI":"10.1109\/TPAMI.2022.3182052","volume":"45","author":"Y Gao","year":"2022","unstructured":"Gao Y, Feng Y, Ji S, Ji R. Hgnn+: general hypergraph neural networks. IEEE Trans Pattern Anal Mach Intell. 2022;45(3):3181\u201399.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"3453_CR19","unstructured":"Yadati N, Nimishakavi M, Yadav P, Nitin V, Louis A, Talukdar P. HyperGCN: a new method of training graph convolutional networks on hypergraphs. In: Proceedings of the 33rd international conference on neural information processing systems. 2019. p. 1511\u20131522."},{"key":"3453_CR20","unstructured":"Dong Y, Sawin W, Bengio Y. Hnhn: hypergraph networks with hyperedge neurons. arXiv preprint arXiv:2006.12278 2020."},{"key":"3453_CR21","doi-asserted-by":"crossref","unstructured":"Huang J, Yang J. Unignn: a unified framework for graph and hypergraph neural networks. arXiv preprint arXiv:2105.00956 2021.","DOI":"10.24963\/ijcai.2021\/353"},{"key":"3453_CR22","doi-asserted-by":"publisher","first-page":"12809","DOI":"10.1007\/s00521-024-09960-z","volume":"36","author":"BP Bhuyan","year":"2023","unstructured":"Bhuyan BP, Ramdane-Cherif A, Tomar R, Singh T. Neuro-symbolic artificial intelligence: a survey. Neural Comput Appl. 2024;36:12809\u201312844.","journal-title":"Neural Comput Appl"},{"issue":"3","key":"3453_CR23","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/MIS.2023.3268724","volume":"38","author":"A Sheth","year":"2023","unstructured":"Sheth A, Roy K, Gaur M. Neurosymbolic artificial intelligence (why, what, and how). IEEE Intell Syst. 2023;38(3):56\u201362.","journal-title":"IEEE Intell Syst"},{"issue":"11","key":"3453_CR24","doi-asserted-by":"publisher","first-page":"12387","DOI":"10.1007\/s10462-023-10448-w","volume":"56","author":"AD Garcez","year":"2023","unstructured":"Garcez AD, Lamb LC. Neurosymbolic ai: the 3rd wave. Artif Intell Rev. 2023;56(11):12387\u2013406.","journal-title":"Artif Intell Rev"},{"key":"3453_CR25","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.neucom.2023.02.026","volume":"531","author":"Z Yi","year":"2023","unstructured":"Yi Z, Lian J, Liu Q, Zhu H, Liang D, Liu J. Learning rules in spiking neural networks: a survey. Neurocomputing. 2023;531:163\u201379.","journal-title":"Neurocomputing"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03453-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-03453-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-03453-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T07:16:58Z","timestamp":1733123818000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-03453-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,2]]},"references-count":25,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["3453"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-03453-5","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2024,12,2]]},"assertion":[{"value":"28 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2024","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 declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Research involving human and \/or animals"}}],"article-number":"1124"}}