{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T15:10:01Z","timestamp":1750173001412,"version":"3.41.0"},"reference-count":56,"publisher":"Springer Science and Business Media LLC","issue":"18","license":[{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T00:00:00Z","timestamp":1742428800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2020YFC1523202"],"award-info":[{"award-number":["2020YFC1523202"]}],"id":[{"id":"10.13039\/501100012166","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":[[2025,6]]},"DOI":"10.1007\/s00521-025-11133-5","type":"journal-article","created":{"date-parts":[[2025,3,20]],"date-time":"2025-03-20T20:06:23Z","timestamp":1742501183000},"page":"11821-11841","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Heterogeneous graph multi-level semantics extraction for node classification"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-0480-4313","authenticated-orcid":false,"given":"Haochang","family":"Hao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4939-3880","authenticated-orcid":false,"given":"Jun","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8710-3296","authenticated-orcid":false,"given":"Shuzhen","family":"Rao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,20]]},"reference":[{"issue":"1","key":"11133_CR1","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1109\/TKDE.2016.2598561","volume":"29","author":"C Shi","year":"2016","unstructured":"Shi C, Li Y, Zhang J, Sun Y, Philip SY (2016) A survey of heterogeneous information network analysis. IEEE Trans Knowl Data Eng 29(1):17\u201337","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11133_CR2","doi-asserted-by":"crossref","unstructured":"Tran DH, Sheng QZ, Zhang WE, Aljubairy A, Zaib M, Hamad SA, Tran NH, Khoa NLD (2021) Hetegraph: graph learning in recommender systems via graph convolutional networks. Neural Comput Appl 1\u201317","DOI":"10.1109\/IJCNN48605.2020.9207078"},{"issue":"11","key":"11133_CR3","doi-asserted-by":"publisher","first-page":"8723","DOI":"10.1007\/s00521-021-06863-1","volume":"34","author":"B Zhang","year":"2022","unstructured":"Zhang B, Guo X, Tu Z, Zhang J (2022) Graph alternate learning for robust graph neural networks in node classification. Neural Comput Appl 34(11):8723\u20138735","journal-title":"Neural Comput Appl"},{"issue":"2","key":"11133_CR4","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1109\/TBDATA.2022.3177455","volume":"9","author":"X Wang","year":"2022","unstructured":"Wang X, Bo D, Shi C, Fan S, Ye Y, Philip SY (2022) A survey on heterogeneous graph embedding: methods, techniques, applications and sources. IEEE Trans Big Data 9(2):415\u2013436","journal-title":"IEEE Trans Big Data"},{"issue":"5","key":"11133_CR5","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/TKDE.2018.2849727","volume":"31","author":"P Cui","year":"2018","unstructured":"Cui P, Wang X, Pei J, Zhu W (2018) A survey on network embedding. IEEE Trans Knowl Data Eng 31(5):833\u2013852","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"11133_CR6","unstructured":"Ren Y, Liu B, Huang C, Dai P, Bo L, Zhang J (2019) Heterogeneous deep graph infomax. arXiv preprint arXiv:1911.08538"},{"key":"11133_CR7","doi-asserted-by":"crossref","unstructured":"Xie T, Wang B, Kuo C-CJ (2022) Graphhop: an enhanced label propagation method for node classification. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2022.3157746"},{"key":"11133_CR8","doi-asserted-by":"crossref","unstructured":"Wang Y, Cao J, Tao H (2021) Graph convolutional network with multi-similarity attribute matrices fusion for node classification. Neural Comput Appl 1\u201311","DOI":"10.1007\/s00521-021-06429-1"},{"key":"11133_CR9","unstructured":"Rusch TK, Bronstein MM, Mishra S (2023) A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993"},{"key":"11133_CR10","doi-asserted-by":"crossref","unstructured":"Yao D, Hu H, Du L, Cong G, Han S, Bi J (2022) Trajgat: a graph-based long-term dependency modeling approach for trajectory similarity computation. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining, pp 2275\u20132285","DOI":"10.1145\/3534678.3539358"},{"key":"11133_CR11","doi-asserted-by":"crossref","unstructured":"Shen Y, Li D, Nan D (2022) Modeling path information for knowledge graph completion. Neural Comput Appl 1\u201311","DOI":"10.1007\/s00521-021-06460-2"},{"key":"11133_CR12","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"},{"issue":"9","key":"11133_CR13","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":"11133_CR14","first-page":"16211","volume":"33","author":"G Nikolentzos","year":"2020","unstructured":"Nikolentzos G, Vazirgiannis M (2020) Random walk graph neural networks. Adv Neural Inf Process Syst 33:16211\u201316222","journal-title":"Adv Neural Inf Process Syst"},{"issue":"11","key":"11133_CR15","doi-asserted-by":"publisher","first-page":"992","DOI":"10.14778\/3402707.3402736","volume":"4","author":"Y Sun","year":"2011","unstructured":"Sun Y, Han J, Yan X, Yu PS, Wu T (2011) Pathsim: meta path-based top-k similarity search in heterogeneous information networks. Proc VLDB Endowm 4(11):992\u20131003","journal-title":"Proc VLDB Endowm"},{"key":"11133_CR16","doi-asserted-by":"crossref","unstructured":"Liang X, Ma Y, Cheng G, Fan C, Yang Y, Liu Z (2022) Meta-path-based heterogeneous graph neural networks in academic network. Int J Mach Learn Cybern 1\u201317","DOI":"10.1007\/s13042-021-01465-8"},{"key":"11133_CR17","doi-asserted-by":"crossref","unstructured":"Sun L, He L, Huang Z, Cao B, Xia C, Wei X, Philip SY (2018) Joint embedding of meta-path and meta-graph for heterogeneous information networks. In: 2018 IEEE international conference on big knowledge (ICBK). IEEE, pp 131\u2013138","DOI":"10.1109\/ICBK.2018.00025"},{"key":"11133_CR18","doi-asserted-by":"crossref","unstructured":"Yu L, Sun L, Du B, Zhu T, Lv W (2022) Label-enhanced graph neural network for semi-supervised node classification. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2022.3231660"},{"key":"11133_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110930","volume":"279","author":"M Guan","year":"2023","unstructured":"Guan M, Cai X, Shang J, Hao F, Liu D, Jiao X, Ni W (2023) Hmsg: heterogeneous graph neural network based on metapath subgraph learning. Knowl-Based Syst 279:110930","journal-title":"Knowl-Based Syst"},{"key":"11133_CR20","doi-asserted-by":"crossref","unstructured":"Sun Y, Zhu D, Du H, Tian Z (2022) Mhnf: multi-hop heterogeneous neighborhood information fusion graph representation learning. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2022.3186158"},{"key":"11133_CR21","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.ins.2023.03.034","volume":"632","author":"C Li","year":"2023","unstructured":"Li C, Yan Y, Fu J, Zhao Z, Zeng Q (2023) Hetregat-fc: heterogeneous residual graph attention network via feature completion. Inf Sci 632:424\u2013438","journal-title":"Inf Sci"},{"key":"11133_CR22","doi-asserted-by":"crossref","unstructured":"Jin D, Huo C, Liang C, Yang L (2021) Heterogeneous graph neural network via attribute completion. In: Proceedings of the web conference 2021, pp 391\u2013400","DOI":"10.1145\/3442381.3449914"},{"key":"11133_CR23","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":"11133_CR24","doi-asserted-by":"crossref","unstructured":"Fan S, Zhu J, Han X, Shi C, Hu L, Ma B, Li Y (2019) Metapath-guided heterogeneous graph neural network for intent recommendation. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 2478\u20132486","DOI":"10.1145\/3292500.3330673"},{"key":"11133_CR25","unstructured":"Kipf TN, Welling M (2016) Variational graph auto-encoders. arXiv preprint arXiv:1611.07308"},{"key":"11133_CR26","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3\u20137, 2018, Proceedings 15. Springer, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"11133_CR27","unstructured":"Zhang M, Chen Y (2018) Link prediction based on graph neural networks. Adv Neural Inf Process Syst 31"},{"key":"11133_CR28","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"11133_CR29","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":"11133_CR30","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":"11133_CR31","doi-asserted-by":"crossref","unstructured":"Lin Y, Liu Z, Luan H, Sun M, Rao S, Liu S (2015) Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379","DOI":"10.18653\/v1\/D15-1082"},{"key":"11133_CR32","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Dur\u00e1n A, Bordes A, Usunier N (2015) Composing relationships with translations. PhD thesis, CNRS, Heudiasyc","DOI":"10.18653\/v1\/D15-1034"},{"key":"11133_CR33","doi-asserted-by":"crossref","unstructured":"Zhang M, Wang Q, Xu W, Li W, Sun S (2018) Discriminative path-based knowledge graph embedding for precise link prediction. In: Advances in information retrieval: 40th european conference on IR Research, ECIR 2018, Grenoble, France, March 26-29, 2018, Proceedings 40. Springer, pp 276\u2013288","DOI":"10.1007\/978-3-319-76941-7_21"},{"key":"11133_CR34","doi-asserted-by":"crossref","unstructured":"Niu G, Zhang Y, Li B, Cui P, Liu S, Li J, Zhang X (2020) Rule-guided compositional representation learning on knowledge graphs. In: Proceedings of the AAAI conference on artificial intelligence, vol 34, pp 2950\u20132958","DOI":"10.1609\/aaai.v34i03.5687"},{"key":"11133_CR35","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"key":"11133_CR36","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781"},{"key":"11133_CR37","doi-asserted-by":"crossref","unstructured":"Wang X, Zhang Y, Shi C (2019) Hyperbolic heterogeneous information network embedding. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 5337\u20135344","DOI":"10.1609\/aaai.v33i01.33015337"},{"key":"11133_CR38","unstructured":"Shang J, Qu M, Liu J, Kaplan LM, Han J, Peng J (2016) Meta-path guided embedding for similarity search in large-scale heterogeneous information networks. arXiv preprint arXiv:1610.09769"},{"key":"11133_CR39","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":"11133_CR40","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 2020, pp 2331\u20132341","DOI":"10.1145\/3366423.3380297"},{"key":"11133_CR41","doi-asserted-by":"crossref","unstructured":"Yang H, Liu J (2021) Knowledge graph representation learning as groupoid: unifying transe, rotate, quate, complex. In: Proceedings of the 30th ACM international conference on information & knowledge management, pp 2311\u20132320","DOI":"10.1145\/3459637.3482442"},{"key":"11133_CR42","unstructured":"Loconte L, Di Mauro N, Peharz R, Vergari A (2024) How to turn your knowledge graph embeddings into generative models. Adv Neural Inf Process Syst 36"},{"key":"11133_CR43","doi-asserted-by":"crossref","unstructured":"Huang Z, Li B, Yin J (2018) Knowledge graph embedding via multiplicative interaction. In: Proceedings of the 2nd international conference on innovation in artificial intelligence, pp 138\u2013142","DOI":"10.1145\/3194206.3194227"},{"key":"11133_CR44","doi-asserted-by":"crossref","unstructured":"Yang L, Kang Z, Cao X, Jin D, Yang B, Guo Y (2019) Topology optimization based graph convolutional network. In: IJCAI, pp 4054\u20134061","DOI":"10.24963\/ijcai.2019\/563"},{"key":"11133_CR45","unstructured":"Wang Y, Jin J, Zhang W, Yu Y, Zhang Z, Wipf D (2021) Bag of tricks for node classification with graph neural networks. arXiv preprint arXiv:2103.13355"},{"key":"11133_CR46","doi-asserted-by":"crossref","unstructured":"Shi Y, Huang Z, Feng S, Zhong H, Wang W, Sun Y (2021) Masked label prediction: Unified message passing model for semi-supervised classification. In: Zhou Z-H (ed) Proceedings of the thirtieth international joint conference on artificial intelligence, IJCAI-21. International Joint Conferences on Artificial Intelligence Organization, pp 1548\u20131554","DOI":"10.24963\/ijcai.2021\/214"},{"key":"11133_CR47","unstructured":"Chen D, Liu X, Lin Y, Li P, Zhou J, Su Q, Sun X (2019) Highwaygraph: Modelling long-distance node relations for improving general graph neural network. arXiv preprint arXiv:1911.03904"},{"key":"11133_CR48","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Adv Neural Inf Process Syst 30"},{"key":"11133_CR49","doi-asserted-by":"crossref","unstructured":"Wang Y, Duan Z, Liao B, Wu F, Zhuang Y (2019) Heterogeneous attributed network embedding with graph convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence, vol 33, pp 10061\u201310062","DOI":"10.1609\/aaai.v33i01.330110061"},{"key":"11133_CR50","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint arXiv:1710.10903"},{"key":"11133_CR51","doi-asserted-by":"crossref","unstructured":"Ramchoun H, Ghanou Y, Ettaouil M, Janati Idrissi MA (2016) Multilayer perceptron: Architecture optimization and training","DOI":"10.9781\/ijimai.2016.415"},{"issue":"1","key":"11133_CR52","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","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"11133_CR53","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"},{"key":"11133_CR54","doi-asserted-by":"crossref","unstructured":"Wang R, Shi C, Zhao T, Wang X, Ye YF (2021) Heterogeneous information network embedding with adversarial disentangler. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2021.3096231"},{"key":"11133_CR55","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Adv Neural Inf Process Syst 32"},{"issue":"4","key":"11133_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3494558","volume":"16","author":"C Huang","year":"2022","unstructured":"Huang C, Fang Y, Lin X, Cao X, Zhang W (2022) Able: meta-path prediction in heterogeneous information networks. ACM Trans Knowl Discov Data (TKDD) 16(4):1\u201321","journal-title":"ACM Trans Knowl Discov Data (TKDD)"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11133-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11133-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11133-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T14:54:12Z","timestamp":1750172052000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11133-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,20]]},"references-count":56,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["11133"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11133-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2025,3,20]]},"assertion":[{"value":"3 January 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2025","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 conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}