{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T03:38:49Z","timestamp":1768793929130,"version":"3.49.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"21","license":[{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T00:00:00Z","timestamp":1691539200000},"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":["61373165"],"award-info":[{"award-number":["61373165"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61832014"],"award-info":[{"award-number":["61832014"]}],"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":[[2023,11]]},"DOI":"10.1007\/s10489-023-04840-w","type":"journal-article","created":{"date-parts":[[2023,8,9]],"date-time":"2023-08-09T08:02:35Z","timestamp":1691568155000},"page":"25626-25639","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["An interlayer feature fusion-based heterogeneous graph neural network"],"prefix":"10.1007","volume":"53","author":[{"given":"Ke","family":"Feng","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6261-576X","authenticated-orcid":false,"given":"Guozheng","family":"Rao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3039-6160","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4492-9052","authenticated-orcid":false,"given":"Qing","family":"Cong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,9]]},"reference":[{"key":"4840_CR1","doi-asserted-by":"crossref","unstructured":"Xie Y, Yu B, Lv S, Zhang C, Gong M (2021) A survey on heterogeneous network representation learning. Pattern Recognit 116(7:107936","DOI":"10.1016\/j.patcog.2021.107936"},{"issue":"2","key":"4840_CR2","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1145\/2481244.2481248","volume":"14","author":"Y Sun","year":"2013","unstructured":"Sun Y, Han J (2013) Mining heterogeneous information networks: a analysis approach. ACM SIGKDD Explorations Newsl 14(2):20\u201328","journal-title":"ACM SIGKDD Explorations Newsl"},{"key":"4840_CR3","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":"4840_CR4","doi-asserted-by":"publisher","first-page":"4456","DOI":"10.1609\/aaai.v33i01.33014456","volume":"33","author":"Y Lu","year":"2019","unstructured":"Lu Y, Shi C, Hu L, Liu Z (2019) Relation structure-aware heterogeneous information network embedding. Proceedings of the AAAI Conference on Artificial Intelligence 33:4456\u20134463","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4840_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107126","volume":"100","author":"Y Zheng","year":"2020","unstructured":"Zheng Y, Hu R, Fung S-f, Yu C, Long G, Guo T, Pan S (2020) Clustering social audiences in business information networks. Pattern Recognit 100:107126. https:\/\/doi.org\/10.1016\/j.patcog.2019.107126","journal-title":"Pattern Recognit"},{"issue":"7","key":"4840_CR6","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1016\/j.cell.2018.05.015","volume":"173","author":"DM Camacho","year":"2018","unstructured":"Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ (2018) Next-generation machine learning for biological networks. Cell 173(7):1581\u20131592. https:\/\/doi.org\/10.1016\/j.cell.2018.05.015","journal-title":"Cell"},{"issue":"6","key":"4840_CR7","doi-asserted-by":"publisher","first-page":"1166","DOI":"10.1109\/TKDE.2018.2851586","volume":"31","author":"EG Tajeuna","year":"2018","unstructured":"Tajeuna EG, Bouguessa M, Wang S (2018) Modeling and predicting community structure changes in time-evolving social networks. IEEE Trans Knowl Data Eng 31(6):1166\u20131180","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4840_CR8","first-page":"391","volume":"2021","author":"D Jin","year":"2021","unstructured":"Jin D, Huo C, Liang C, Yang L (2021) Heterogeneous graph neural network via attribute completion. Proceedings of the Web Conference 2021:391\u2013400","journal-title":"Proceedings of the Web Conference"},{"issue":"1","key":"4840_CR9","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61\u201380","journal-title":"IEEE Trans Neural Netw"},{"issue":"1","key":"4840_CR10","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":"4840_CR11","first-page":"2331","volume":"2020","author":"X Fu","year":"2020","unstructured":"Fu X, Zhang J, Meng Z, King I (2020) Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. Proceedings of The Web Conference 2020:2331\u20132341","journal-title":"Proceedings of The Web Conference"},{"key":"4840_CR12","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"4840_CR13","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR"},{"key":"4840_CR14","doi-asserted-by":"publisher","first-page":"4697","DOI":"10.1609\/aaai.v35i5.16600","volume":"35","author":"J Zhao","year":"2021","unstructured":"Zhao J, Wang X, Shi C, Hu B, Song G, Ye Y (2021) Heterogeneous graph structure learning for graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence 35:4697\u20134705","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4840_CR15","doi-asserted-by":"crossref","unstructured":"Shao Z, Xu Y, Wei W, Wang F, Zhang Z, Zhu F (2022) Heterogeneous graph neural network with multi-view representation learning. IEEE Trans Knowl Data Eng","DOI":"10.1109\/TKDE.2022.3224193"},{"key":"4840_CR16","doi-asserted-by":"crossref","unstructured":"Lv Q, Ding M, Liu Q, Chen Y, Feng W, He S, Zhou C, Jiang J, Dong Y, Tang J (2021) Are we really making much progress? revisiting, benchmarking and refining heterogeneous graph neural networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 1150\u20131160","DOI":"10.1145\/3447548.3467350"},{"key":"4840_CR17","doi-asserted-by":"publisher","first-page":"4132","DOI":"10.1609\/aaai.v34i04.5833","volume":"34","author":"H Hong","year":"2020","unstructured":"Hong H, Guo H, Lin Y, Yang X, Li Z, Ye J (2020) An attentionbased graph neural network for heterogeneous structural learning. Proceedings of the AAAI Conference on Artificial Intelligence 34:4132\u20134139","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4840_CR18","first-page":"2704","volume":"2020","author":"Z Hu","year":"2020","unstructured":"Hu Z, Dong Y, Wang K, Sun Y (2020) Heterogeneous graph transformer. Proceedings of The Web Conference 2020:2704\u20132710","journal-title":"Proceedings of The Web Conference"},{"issue":"5","key":"4840_CR19","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":"4840_CR20","doi-asserted-by":"publisher","first-page":"232","DOI":"10.1609\/aaai.v35i1.16097","volume":"35","author":"H Ji","year":"2021","unstructured":"Ji H, Zhu J, Wang X, Shi C, Wang B, Tan X, Li Y, He S (2021) Who you would like to share with? a study of share recommendation in social e-commerce. Proceedings of the AAAI Conference on Artificial Intelligence 35:232\u2013239","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4840_CR21","doi-asserted-by":"crossref","unstructured":"Chen T, Zhou K, Duan K, Zheng W, Wang P, Hu X, Wang Z (2022) Bag of tricks for training deeper graph neural networks: A comprehensive benchmark study. IEEE Trans Pattern Anal Mach Intell","DOI":"10.1109\/TPAMI.2022.3174515"},{"key":"4840_CR22","doi-asserted-by":"crossref","unstructured":"Wu L, Cui P, Pei J, Zhao L, Guo X (2022) Graph neural networks: foundation, frontiers and applications. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4840\u20134841","DOI":"10.1145\/3534678.3542609"},{"issue":"2","key":"4840_CR23","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","volume":"31","author":"C Shi","year":"2018","unstructured":"Shi C, Hu B, Zhao WX, Philip SY (2018) Heterogeneous information network embedding for recommendation. IEEE Trans Knowl Data Eng 31(2):357\u2013370","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4840_CR24","doi-asserted-by":"crossref","unstructured":"Tang J, Qu M, Mei Q (2015) Pte: Predictive text embedding through largescale heterogeneous text networks. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1165\u20131174","DOI":"10.1145\/2783258.2783307"},{"issue":"1s","key":"4840_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3419842","volume":"17","author":"J Xiao","year":"2021","unstructured":"Xiao J, Xu H, Gao H, Bian M, Li Y (2021) A weakly supervised semantic segmentation network by aggregating seed cues: the multi-object proposal generation perspective. ACM Trans Multimidia Comput Commun Appl 17(1s):1\u201319","journal-title":"ACM Trans Multimidia Comput Commun Appl"},{"key":"4840_CR26","doi-asserted-by":"crossref","unstructured":"Xu L, Wei X, Cao J, Yu PS (2017) Embedding of embedding (eoe) joint embedding for coupled heterogeneous networks. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 741\u2013749","DOI":"10.1145\/3018661.3018723"},{"issue":"9","key":"4840_CR27","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":"4840_CR28","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":"4840_CR29","doi-asserted-by":"crossref","unstructured":"Fu T-Y, Lee W-C, Lei Z (2017) Hin2vec: Explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1797\u20131806","DOI":"10.1145\/3132847.3132953"},{"key":"4840_CR30","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang C (2017) Hine: Heterogeneous information network embedding. In: International Conference on Database Systems for Advanced Applications, pp. 180\u2013195. Springer","DOI":"10.1007\/978-3-319-55753-3_12"},{"key":"4840_CR31","doi-asserted-by":"crossref","unstructured":"Zhang J, Xia C, Zhang C, Cui L, Fu Y, Philip SY (2017) Bl-mne: emerging heterogeneous social network embedding through broad learning with aligned autoencoder. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 605\u2013614. IEEE","DOI":"10.1109\/ICDM.2017.70"},{"key":"4840_CR32","doi-asserted-by":"crossref","unstructured":"Wang H, Zhang F, Hou M, Xie X, Guo M, Liu Q (2018) Shine: Signed heterogeneous information network embedding for sentiment link prediction. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 592\u2013600","DOI":"10.1145\/3159652.3159666"},{"key":"4840_CR33","doi-asserted-by":"crossref","unstructured":"Hu B, Fang Y, Shi C (2019) Adversarial learning on heterogeneous information networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 120\u2013129","DOI":"10.1145\/3292500.3330970"},{"key":"4840_CR34","doi-asserted-by":"crossref","unstructured":"Gao H, Dai B, Miao H, Yang X, Barroso RJD, Walayat H (2022) A novel gapg approach to automatic property generation for formal verification: The gan perspective. ACM Trans Multi Comput Commun Appl (TOMM)","DOI":"10.1145\/3517154"},{"key":"4840_CR35","doi-asserted-by":"crossref","unstructured":"Zhao K, Bai T, Wu, B, Wang B, Zhang Y, Yang Y, Nie J-Y (20202) Deep adversarial completion for sparse heterogeneous information network embedding. In: Proceedings of The Web Conference 2020, pp. 508\u2013518","DOI":"10.1145\/3366423.3380134"},{"key":"4840_CR36","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"},{"issue":"1","key":"4840_CR37","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/TCBB.2020.2991173","volume":"18","author":"J Chen","year":"2020","unstructured":"Chen J, Ying H, Liu X, Gu J, Feng R, Chen T, Gao H, Wu J (2020) A transfer learning based super-resolution microscopy for biopsy slice images: the joint methods perspective. IEEE\/ACM Trans Computat Biol Bioinform 18(1):103\u2013113","journal-title":"IEEE\/ACM Trans Computat Biol Bioinform"},{"issue":"1","key":"4840_CR38","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1109\/TCSS.2021.3102591","volume":"9","author":"H Gao","year":"2021","unstructured":"Gao H, Xu K, Cao M, Xiao J, Xu Q, Yin Y (2021) The deep features and attention mechanism-based method to dish healthcare under social iot systems: An empirical study with a hand-deep local-global net. IEEE Trans Computat Soc Syst 9(1):336\u2013347","journal-title":"IEEE Trans Computat Soc Syst"},{"key":"4840_CR39","doi-asserted-by":"crossref","unstructured":"Gao H, Xiao J, Yin Y, Liu T, Shi J (2022) A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples. IEEE Trans Neural Netw Learn Syst","DOI":"10.1109\/TNNLS.2022.3155486"},{"key":"4840_CR40","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: European Semantic Web Conference, pp. 593\u2013607. Springer","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"4840_CR41","first-page":"11983","volume":"32","author":"S Yun","year":"2019","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Advances in Neural Information Process Syst 32:11983\u201311993","journal-title":"Advances in Neural Information Process Syst"},{"key":"4840_CR42","doi-asserted-by":"crossref","unstructured":"Zhu S, Zhou C, Pan S, Zhu X, Wang B (2019) Relation structureaware heterogeneous graph neural network. In: 2019 IEEE International Conference on Data Mining (ICDM), pp. 1534\u20131539. IEEE","DOI":"10.1109\/ICDM.2019.00203"},{"key":"4840_CR43","doi-asserted-by":"crossref","unstructured":"Ma S, Liu J-W, Zuo X, Li W-M (2021) Heterogeneous graph gated attention network. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20136. IEEE","DOI":"10.1109\/IJCNN52387.2021.9533711"},{"key":"4840_CR44","doi-asserted-by":"crossref","unstructured":"Tao Y, Li Y, Wu Z (2021) Revisiting graph neural networks for node classification in heterogeneous graphs. In: 2021 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE","DOI":"10.1109\/ICME51207.2021.9428354"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04840-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-04840-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-04840-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T22:32:23Z","timestamp":1729895543000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-04840-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,9]]},"references-count":44,"journal-issue":{"issue":"21","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["4840"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-04840-w","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,9]]},"assertion":[{"value":"25 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 August 2023","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 conflicts of interest","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"Not applicable","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}