{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T13:50:22Z","timestamp":1758289822096,"version":"3.44.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"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":["72192823;61972355"],"award-info":[{"award-number":["72192823;61972355"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s10489-025-06622-y","type":"journal-article","created":{"date-parts":[[2025,6,12]],"date-time":"2025-06-12T00:08:33Z","timestamp":1749686913000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NSE: Node sequence encoding for link prediction in heterogeneous graphs"],"prefix":"10.1007","volume":"55","author":[{"given":"Ying","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenggang","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9257-8525","authenticated-orcid":false,"given":"Ji","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,12]]},"reference":[{"key":"6622_CR1","doi-asserted-by":"crossref","unstructured":"Huang C, Xu H, Xu Y, Dai P, Xia L, Lu M, Bo L, Xing H, Lai X, Ye Y (2021) Knowledge-aware coupled graph neural network for social recommendation. In: Proceedings of the AAAI conference on artificial intelligence vol 35, pp 4115\u20134122","DOI":"10.1609\/aaai.v35i5.16533"},{"key":"6622_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126441","volume":"549","author":"X Li","year":"2023","unstructured":"Li X, Sun L, Ling M, Peng Y (2023) A survey of graph neural network based recommendation in social networks. Neurocomputing 549:126441","journal-title":"Neurocomputing"},{"key":"6622_CR3","doi-asserted-by":"crossref","unstructured":"Friji H, Olivereau A, Sarkiss M (2023) Efficient network representation for gnn-based intrusion detection. In: International conference on applied cryptography and network security, pp 532\u2013554. Springer","DOI":"10.1007\/978-3-031-33488-7_20"},{"key":"6622_CR4","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"6622_CR5","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1016\/j.physa.2017.04.126","volume":"482","author":"J Li","year":"2017","unstructured":"Li J, Ge B, Yang K, Chen Y, Tan Y (2017) Meta-path based heterogeneous combat network link prediction. Phys A: Stat Mech its Appl 482:507\u2013523","journal-title":"Phys A: Stat Mech its Appl"},{"key":"6622_CR6","doi-asserted-by":"crossref","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the web conference vol 2020, pp 2331\u20132341","DOI":"10.1145\/3366423.3380297"},{"key":"6622_CR7","doi-asserted-by":"crossref","unstructured":"Huang Z, Zheng Y, Cheng R, Sun Y, Mamoulis N, Li X (2016) Meta structure: Computing relevance in large heterogeneous information networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1595\u20131604","DOI":"10.1145\/2939672.2939815"},{"key":"6622_CR8","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neucom.2021.10.001","volume":"468","author":"G Mei","year":"2022","unstructured":"Mei G, Pan L, Liu S (2022) Heterogeneous graph embedding by aggregating meta-path and meta-structure through attention mechanism. Neurocomputing 468:276\u2013285","journal-title":"Neurocomputing"},{"key":"6622_CR9","first-page":"5862","volume":"33","author":"K Huang","year":"2020","unstructured":"Huang K, Zitnik M (2020) Graph meta learning via local subgraphs. Adv Neural Inf Process Syst 33:5862\u20135874","journal-title":"Adv Neural Inf Process Syst"},{"key":"6622_CR10","unstructured":"Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Adv Neural Inf Process Syst 31"},{"key":"6622_CR11","doi-asserted-by":"crossref","unstructured":"Shi C, Ji H, Lu Z, Tang Y, Li P, Yang C (2023) Distance information improves heterogeneous graph neural networks. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2023.3300879"},{"key":"6622_CR12","first-page":"4465","volume":"33","author":"P Li","year":"2020","unstructured":"Li P, Wang Y, Wang H, Leskovec J (2020) Distance encoding: Design provably more powerful neural networks for graph representation learning. Adv Neural Inf Process Syst 33:4465\u20134478","journal-title":"Adv Neural Inf Process Syst"},{"issue":"8","key":"6622_CR13","doi-asserted-by":"publisher","first-page":"8003","DOI":"10.1007\/s10462-022-10375-2","volume":"56","author":"R Bing","year":"2023","unstructured":"Bing R, Yuan G, Zhu M, Meng F, Ma H, Qiao S (2023) Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications. Artif Intell Rev 56(8):8003\u20138042","journal-title":"Artif Intell Rev"},{"key":"6622_CR14","first-page":"14143","volume":"33","author":"C Vignac","year":"2020","unstructured":"Vignac C, Loukas A, Frossard P (2020) Building powerful and equivariant graph neural networks with structural message-passing. Adv Neural Inf Process Syst 33:14143\u201314155","journal-title":"Adv Neural Inf Process Syst"},{"key":"6622_CR15","unstructured":"Kipf T.N, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations"},{"key":"6622_CR16","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30"},{"key":"6622_CR17","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations"},{"key":"6622_CR18","doi-asserted-by":"crossref","unstructured":"Yang X, Yan M, Pan S, Ye X, Fan D (2023) Simple and efficient heterogeneous graph neural network. In: Proceedings of the AAAI conference on artificial intelligence vol 37, pp 10816\u201310824","DOI":"10.1609\/aaai.v37i9.26283"},{"key":"6622_CR19","doi-asserted-by":"crossref","unstructured":"Zhao J, Wang X, Shi C, Hu B, Song G, Ye Y (2021) Heterogeneous graph structure learning for graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence vol 35, pp 4697\u20134705","DOI":"10.1609\/aaai.v35i5.16600"},{"key":"6622_CR20","doi-asserted-by":"crossref","unstructured":"Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu PS (2019) Heterogeneous graph attention network. In: The world wide web conference, pp 2022\u20132032","DOI":"10.1145\/3308558.3313562"},{"key":"6622_CR21","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp 855\u2013864","DOI":"10.1145\/2939672.2939754"},{"key":"6622_CR22","doi-asserted-by":"crossref","unstructured":"Ju W, Fang Z, Gu Y, Liu Z, Long Q, Qiao Z, Qin Y, Shen J, Sun F, Xiao Z, et al (2024) A comprehensive survey on deep graph representation learning. Neural Networks, 106207","DOI":"10.1016\/j.neunet.2024.106207"},{"issue":"1","key":"6622_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3633518","volume":"15","author":"S Khoshraftar","year":"2024","unstructured":"Khoshraftar S, An A (2024) A survey on graph representation learning methods. ACM Trans Intell SystTechnol 15(1):1\u201355","journal-title":"ACM Trans Intell SystTechnol"},{"key":"6622_CR24","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","DOI":"10.1145\/2623330.2623732"},{"key":"6622_CR25","doi-asserted-by":"crossref","unstructured":"Chivukula R, Lakshmi TJ (2021) Mining heterogeneous information networks: a review. In: 2021 IEEE pune section international conference (PuneCon), pp 1\u20134. IEEE","DOI":"10.1109\/PuneCon52575.2021.9686506"},{"key":"6622_CR26","doi-asserted-by":"crossref","unstructured":"Cui Y, Sun Z, Hu W (2025) Transfer-and-fusion: Integrated link prediction across knowledge graphs. IEEE Transactions on Knowledge and Data Engineering","DOI":"10.1109\/TKDE.2025.3544255"},{"key":"6622_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.infsof.2022.107016","volume":"151","author":"Y Gao","year":"2022","unstructured":"Gao Y, Zhu Y, Zhao Y (2022) Dealing with imbalanced data for interpretable defect prediction. Inf Soft Technol 151:107016","journal-title":"Inf Soft Technol"},{"key":"6622_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.124012","volume":"251","author":"Y Yang","year":"2024","unstructured":"Yang Y, Xu K, Tang Y (2024) Gan-based self-supervised message passing graph representation learning. Expert Syst Appl 251:124012","journal-title":"Expert Syst Appl"},{"key":"6622_CR29","doi-asserted-by":"crossref","unstructured":"Kumar A, Singh SS, Singh K, Biswas B (2020) Link prediction techniques, applications, and performance: A survey. Phys A: Stati Mech its Appl 553:124289","DOI":"10.1016\/j.physa.2020.124289"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06622-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06622-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06622-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T10:27:17Z","timestamp":1758277637000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06622-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6]]},"references-count":29,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["6622"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06622-y","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"type":"print","value":"0924-669X"},{"type":"electronic","value":"1573-7497"}],"subject":[],"published":{"date-parts":[[2025,6]]},"assertion":[{"value":"9 May 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 June 2025","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 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":"Competing interest statement"}}],"article-number":"738"}}