{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,17]],"date-time":"2026-01-17T21:31:45Z","timestamp":1768685505242,"version":"3.49.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T00:00:00Z","timestamp":1712880000000},"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":["No. 62272487"],"award-info":[{"award-number":["No. 62272487"]}],"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":["No. 62272487"],"award-info":[{"award-number":["No. 62272487"]}],"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":["No. 62272487"],"award-info":[{"award-number":["No. 62272487"]}],"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":["No. 62272487"],"award-info":[{"award-number":["No. 62272487"]}],"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":["No. 62272487"],"award-info":[{"award-number":["No. 62272487"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1007\/s10115-024-02090-x","type":"journal-article","created":{"date-parts":[[2024,4,12]],"date-time":"2024-04-12T18:02:36Z","timestamp":1712944956000},"page":"4283-4308","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Graph neural architecture search with heterogeneous message-passing mechanisms"],"prefix":"10.1007","volume":"66","author":[{"given":"Yili","family":"Wang","sequence":"first","affiliation":[]},{"given":"Jiamin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Qiutong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Changlong","family":"He","sequence":"additional","affiliation":[]},{"given":"Jianliang","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,12]]},"reference":[{"key":"2090_CR1","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 Endow 4:992\u20131003","journal-title":"Proc VLDB Endow"},{"key":"2090_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1109\/TKDE.2019.2924431","volume":"33","author":"Y Yang","year":"2021","unstructured":"Yang Y, Xu Y, Sun Y, Dong Y, Wu F, Zhuang Y (2021) Mining fraudsters and fraudulent strategies in large-scale mobile social networks. IEEE Trans Knowl Data Eng 33:169\u2013179","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2090_CR3","doi-asserted-by":"publisher","first-page":"980","DOI":"10.1109\/TPAMI.2022.3144993","volume":"45","author":"H Peng","year":"2022","unstructured":"Peng H, Zhang R, Li S, Cao Y, Pan S, Yu PS (2022) Reinforced, incremental and cross-lingual event detection from social messages. IEEE Trans Pattern Anal Mach Intell 45:980\u2013998","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2090_CR4","first-page":"1","volume":"40","author":"G Zhang","year":"2022","unstructured":"Zhang G, Li Z, Huang J, Wu J, Zhou C, Yang J, Gao J (2022) Efraudcom: an e-commerce fraud detection system via competitive graph neural networks. ACM Trans Inf Syst 40:1\u201329","journal-title":"ACM Trans Inf Syst"},{"key":"2090_CR5","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 32:4\u201324","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2090_CR6","doi-asserted-by":"crossref","unstructured":"Han Z, Xu F, Shi J, Shang Y, Ma H, Hui P, Li Y (2020) Genetic meta-structure search for recommendation on heterogeneous information network. In: Proceedings of the ACM international conference on information and knowledge management, pp 455\u2013464","DOI":"10.1145\/3340531.3412015"},{"key":"2090_CR7","doi-asserted-by":"crossref","unstructured":"Ding Y, Yao Q, Zhao H, Zhang T (2021) Diffmg: differentiable meta graph search for heterogeneous graph neural networks. In: Proceedings of the ACM SIGKDD conference on knowledge discovery and data mining, pp 279\u2013288","DOI":"10.1145\/3447548.3467447"},{"key":"2090_CR8","first-page":"1","volume":"32","author":"S Yun","year":"2019","unstructured":"Yun S, Jeong M, Kim R, Kang J, Kim HJ (2019) Graph transformer networks. Adv Neural Inf Process Syst 32:1\u201311","journal-title":"Adv Neural Inf Process Syst"},{"key":"2090_CR9","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 ACM SIGKDD international conference on knowledge discovery and data mining, pp 1595\u20131604","DOI":"10.1145\/2939672.2939815"},{"key":"2090_CR10","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 Endow 4:992\u20131003","journal-title":"Proc VLDB Endow"},{"key":"2090_CR11","doi-asserted-by":"crossref","unstructured":"Gao Y, Zhang P, Li Z, Zhou C, Liu Y, Hu Y (2021) Heterogeneous graph neural architecture search. In: IEEE international conference on data mining, pp 1066\u20131071","DOI":"10.1109\/ICDM51629.2021.00124"},{"key":"2090_CR12","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":"2090_CR13","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, pp 2331\u20132341","DOI":"10.1145\/3366423.3380297"},{"key":"2090_CR14","unstructured":"Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations, pp 1\u201314"},{"key":"2090_CR15","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In: International conference on learning representations, pp 1\u201314"},{"key":"2090_CR16","unstructured":"Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. In: Advances in neural information processing systems, vol 30, pp 1\u201311"},{"key":"2090_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113715","volume":"161","author":"T Ma","year":"2020","unstructured":"Ma T, Pan Q, Wang H, Shao W, Tian Y, Al-Nabhan N (2020) Graph classification algorithm based on graph structure embedding. Expert Syst Appl 161:113715","journal-title":"Expert Syst Appl"},{"key":"2090_CR18","doi-asserted-by":"crossref","unstructured":"He X, Deng K, Wang X, Li Y, Zhang Y, Wang M (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the international ACM SIGIR conference on research and development in information retrieval, pp 639\u2013648","DOI":"10.1145\/3397271.3401063"},{"key":"2090_CR19","doi-asserted-by":"crossref","unstructured":"Guo Z, Zhang X, Mu H, Heng W, Liu Z, Wei Y, Sun J (2020) Single path one-shot neural architecture search with uniform sampling. In: Proceedings of the European conference on computer vision, pp 544\u2013560","DOI":"10.1007\/978-3-030-58517-4_32"},{"key":"2090_CR20","doi-asserted-by":"crossref","unstructured":"Chu X, Zhang B, Xu R (2021) Fairnas: Rethinking evaluation fairness of weight sharing neural architecture search. In: International conference on computer vision, pp 12239\u201312248","DOI":"10.1109\/ICCV48922.2021.01202"},{"key":"2090_CR21","unstructured":"Liu H, Simonyan K, Yang Y (2019) Darts: differentiable architecture search. In: International conference on learning representations, pp 1\u201313"},{"key":"2090_CR22","doi-asserted-by":"crossref","unstructured":"Schlichtkrull M, Kipf TN, Bloem P, Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: The semantic web, pp 593\u2013607","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"2090_CR23","doi-asserted-by":"crossref","unstructured":"Zhang S, Xie L (2021) Improving attention mechanism in graph neural networks via cardinality preservation. In: Proceedings of the international joint conference on artificial intelligence","DOI":"10.24963\/ijcai.2020\/194"},{"key":"2090_CR24","doi-asserted-by":"crossref","unstructured":"Jin J, Qin J, Fang Y, Du K, Zhang W, Yu Y, Zhang Z, Smola AJ (2020) An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery & data mining, pp 75\u201384","DOI":"10.1145\/3394486.3403050"},{"key":"2090_CR25","doi-asserted-by":"crossref","unstructured":"Hu Z, Dong Y, Wang K, Sun Y (2020) Heterogeneous graph transformer. In: Proceedings of the web conference, pp 2704\u20132710","DOI":"10.1145\/3366423.3380027"},{"key":"2090_CR26","doi-asserted-by":"crossref","unstructured":"Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph neural network. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 793\u2013803","DOI":"10.1145\/3292500.3330961"},{"key":"2090_CR27","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1109\/TMM.2012.2194993","volume":"14","author":"L Li","year":"2012","unstructured":"Li L, Jiang S, Huang Q (2012) Learning hierarchical semantic description via mixed-norm regularization for image understanding. IEEE Trans Multimed 14:1401\u20131413","journal-title":"IEEE Trans Multimed"},{"key":"2090_CR28","doi-asserted-by":"crossref","unstructured":"Liu X, Li L, Wang S, Zha Z-J, Meng D, Huang Q (2019) Adaptive reconstruction network for weakly supervised referring expression grounding. In: 2019 IEEE\/CVF international conference on computer vision, vol 1, pp 2611\u20132620","DOI":"10.1109\/ICCV.2019.00270"},{"key":"2090_CR29","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1109\/TMM.2019.2912735","volume":"21","author":"S Yang","year":"2019","unstructured":"Yang S, Li L, Wang S, Zhang W, Huang Q, Tian Q (2019) Skeletonnet: a hybrid network with a skeleton-embedding process for multi-view image representation learning. IEEE Trans Multimedia 21:2916\u20132929","journal-title":"IEEE Trans Multimedia"},{"key":"2090_CR30","doi-asserted-by":"crossref","unstructured":"Wei L, Zhao H, He Z (2022) Designing the topology of graph neural networks: a novel feature fusion perspective. In: Proceedings of the web conference, pp 1381\u20131391","DOI":"10.1145\/3485447.3512185"},{"key":"2090_CR31","doi-asserted-by":"publisher","first-page":"3117","DOI":"10.1109\/TPDS.2022.3151895","volume":"33","author":"J Chen","year":"2022","unstructured":"Chen J, Gao J, Chen Y, Oloulade BM, Lyu T, Li Z (2022) Auto-gnas: a parallel graph neural architecture search framework. IEEE Trans Parallel Distrib Syst 33:3117\u20133128","journal-title":"IEEE Trans Parallel Distrib Syst"},{"key":"2090_CR32","doi-asserted-by":"crossref","unstructured":"Cai S, Li L, Deng J, Zhang B, Zha Z-J, Su L, Huang Q (2021) Rethinking graph neural architecture search from message-passing. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6657\u20136666","DOI":"10.1109\/CVPR46437.2021.00659"},{"key":"2090_CR33","unstructured":"Xu K, Hu W, Leskovec J, Jegelka S (2019) How powerful are graph neural networks? In: International conference on learning representations"},{"key":"2090_CR34","doi-asserted-by":"crossref","unstructured":"Zhang J, Shi X, Zhao S, King I (2019) Star-gcn: stacked and reconstructed graph convolutional networks for recommender systems. In: Proceedings of the international joint conference on artificial intelligence, pp 4264\u20134270","DOI":"10.24963\/ijcai.2019\/592"},{"key":"2090_CR35","doi-asserted-by":"crossref","unstructured":"Dong Y, Chawla NV, Swami A (2017) Metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining, pp 135\u2013144","DOI":"10.1145\/3097983.3098036"},{"key":"2090_CR36","first-page":"1","volume":"32","author":"A Paszke","year":"2019","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L (2019) Pytorch: an imperative style, high-performance deep learning library. Adv Neural Inf Process Syst 32:1\u201312","journal-title":"Adv Neural Inf Process Syst"},{"key":"2090_CR37","unstructured":"Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch Geometric. In: ICLR workshop on representation learning on graphs and manifolds, pp 1\u20139"},{"key":"2090_CR38","doi-asserted-by":"crossref","unstructured":"Li Y, Jin Y, Song G, Zhu Z, Shi C, Wang Y (2021) Graphmse: efficient meta-path selection in semantically aligned feature space for graph neural networks. In: Proceedings of the AAAI conference on artificial intelligence, pp 4206\u20134214","DOI":"10.1609\/aaai.v35i5.16544"},{"key":"2090_CR39","unstructured":"Bender G, Kindermans P-J, Zoph B, Vasudevan V, Le Q (2018) Understanding and simplifying one-shot architecture search. In: International conference on machine learning, pp 550\u2013559"},{"key":"2090_CR40","unstructured":"Brock A, Lim T, Ritchie JM, Weston N (2018) Smash: one-shot model architecture search through hypernetworks. In: International conference on learning representations, pp 1\u201322"},{"key":"2090_CR41","doi-asserted-by":"crossref","unstructured":"Real E, Aggarwal A, Huang Y, Le QV (2019) Regularized evolution for image classifier architecture search. In: Proceedings of the AAAI conference on artificial intelligence, pp 4780\u20134789","DOI":"10.1609\/aaai.v33i01.33014780"},{"key":"2090_CR42","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1109\/TNNLS.2021.3100554","volume":"34","author":"Y Liu","year":"2023","unstructured":"Liu Y, Sun Y, Xue B, Zhang M, Yen GG, Tan KC (2023) A survey on evolutionary neural architecture search. IEEE Trans Neural Netw Learn Syst 34:550\u2013570","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02090-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-024-02090-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-024-02090-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T07:10:41Z","timestamp":1718953841000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-024-02090-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,12]]},"references-count":42,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["2090"],"URL":"https:\/\/doi.org\/10.1007\/s10115-024-02090-x","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,12]]},"assertion":[{"value":"16 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2024","order":4,"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 competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}