{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:39:15Z","timestamp":1761194355761,"version":"build-2065373602"},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"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":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s11432-024-4539-1","type":"journal-article","created":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:35:49Z","timestamp":1761194149000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph neural architecture search with large language models"],"prefix":"10.1007","volume":"68","author":[{"given":"Haishuai","family":"Wang","sequence":"first","affiliation":[]},{"given":"Yang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiajun","family":"Bu","sequence":"additional","affiliation":[]},{"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"4539_CR1","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1109\/TKDE.2016.2616305","volume":"29","author":"H Wang","year":"2016","unstructured":"Wang H, Zhang P, Zhu X, et al. Incremental subgraph feature selection for graph classification. IEEE Trans Knowl Data Eng, 2016, 29: 128\u2013142","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4539_CR2","doi-asserted-by":"publisher","first-page":"2404671","DOI":"10.1002\/advs.202404671","volume":"12","author":"Y Gao","year":"2025","unstructured":"Gao Y, Zhang X, Sun Z, et al. Precision adverse drug reactions prediction with heterogeneous graph neural network. Adv Sci, 2025, 12: 2404671","journal-title":"Adv Sci"},{"key":"4539_CR3","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1038\/s41398-024-02972-2","volume":"14","author":"W Chen","year":"2024","unstructured":"Chen W, Yang J, Sun Z, et al. DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data. Transl Psychiatry, 2024, 14: 375","journal-title":"Transl Psychiatry"},{"key":"4539_CR4","doi-asserted-by":"publisher","first-page":"3670","DOI":"10.1145\/3447548.3467065","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","author":"Z Li","year":"2021","unstructured":"Li Z, Wang H, Zhang P, et al. Live-streaming fraud detection: a heterogeneous graph neural network approach. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021. 3670\u20133678"},{"key":"4539_CR5","first-page":"4704","volume-title":"Proceedings of the 30th International Joint Conference on Artificial Intelligence","author":"Z Zhang","year":"2021","unstructured":"Zhang Z, Wang X, Zhu W. Automated machine learning on graphs: a survey. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence, 2021. 4704\u20134712"},{"key":"4539_CR6","first-page":"1403","volume-title":"Proceedings of IJCAI","author":"Y Gao","year":"2020","unstructured":"Gao Y, Yang H, Zhang P, et al. Graph neural architecture search. In: Proceedings of IJCAI, 2020. 1403\u20131409"},{"key":"4539_CR7","first-page":"552","volume-title":"Proceedings of IEEE 37th International Conference on Data Engineering (ICDE)","author":"Z Huan","year":"2021","unstructured":"Huan Z, Quanming Y, Weiwei T. Search to aggregate neighborhood for graph neural network. In: Proceedings of IEEE 37th International Conference on Data Engineering (ICDE), 2021. 552\u2013563"},{"key":"4539_CR8","doi-asserted-by":"publisher","first-page":"108752","DOI":"10.1016\/j.knosys.2022.108752","volume":"247","author":"M Shi","year":"2022","unstructured":"Shi M, Tang Y, Zhu X, et al. Genetic-GNN: evolutionary architecture search for graph neural networks. Knowledge-Based Syst, 2022, 247: 108752","journal-title":"Knowledge-Based Syst"},{"key":"4539_CR9","unstructured":"Zheng M, Su X, You S, et al. Can GPT-4 perform neural architecture search? 2023. ArXiv:2304.10970"},{"key":"4539_CR10","first-page":"6657","volume-title":"Proceedings of CVPR","author":"S Cai","year":"2021","unstructured":"Cai S, Li L, Deng J, et al. Rethinking graph neural architecture search from message-passing. In: Proceedings of CVPR, 2021. 6657\u20136666"},{"key":"4539_CR11","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Wang X, Guan C, et al. AutoGT: automated graph transformer architecture search. In: Proceedings of the 11th International Conference on Learning Representations, 2023"},{"key":"4539_CR12","doi-asserted-by":"publisher","first-page":"1029307","DOI":"10.3389\/fdata.2022.1029307","volume":"5","author":"K Zhou","year":"2022","unstructured":"Zhou K, Huang X, Song Q, et al. Auto-GNN: neural architecture search of graph neural networks. Front Big Data, 2022, 5: 1029307","journal-title":"Front Big Data"},{"key":"4539_CR13","doi-asserted-by":"publisher","first-page":"6973","DOI":"10.1109\/TKDE.2023.3239842","volume":"35","author":"Y Gao","year":"2023","unstructured":"Gao Y, Zhang P, Yang H, et al. GraphNAS++: distributed architecture search for graph neural networks. IEEE Trans Knowl Data Eng, 2023, 35: 6973\u20136987","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4539_CR14","doi-asserted-by":"publisher","first-page":"4021","DOI":"10.1007\/s10115-023-01886-7","volume":"65","author":"J Gao","year":"2023","unstructured":"Gao J, Al-Sabri R, Oloulade B M, et al. GM2NAS: multitask multiview graph neural architecture search. Knowl Inf Syst, 2023, 65: 4021\u20134054","journal-title":"Knowl Inf Syst"},{"key":"4539_CR15","doi-asserted-by":"publisher","first-page":"1221","DOI":"10.1109\/TCBB.2022.3205113","volume":"20","author":"R Al-Sabri","year":"2022","unstructured":"Al-Sabri R, Gao J, Chen J, et al. Multi-view graph neural architecture search for biomedical entity and relation extraction. IEEE ACM Trans Comput Biol Bioinf, 2022, 20: 1221\u20131233","journal-title":"IEEE ACM Trans Comput Biol Bioinf"},{"key":"4539_CR16","first-page":"1066","volume-title":"Proceedings of IEEE International Conference on Data Mining","author":"Y Gao","year":"2021","unstructured":"Gao Y, Zhang P, Li Z, et al. Heterogeneous graph neural architecture search. In: Proceedings of IEEE International Conference on Data Mining, 2021. 1066\u20131071"},{"key":"4539_CR17","doi-asserted-by":"publisher","first-page":"9448","DOI":"10.1109\/TKDE.2023.3239842","volume":"35","author":"Y Gao","year":"2023","unstructured":"Gao Y, Zhang P, Zhou C, et al. HGNAS++: efficient architecture search for heterogeneous graph neural networks. IEEE Trans Knowl Data Eng, 2023, 35: 9448\u20139461","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4539_CR18","first-page":"8510","volume-title":"Proceedings of AAAI","author":"Y Li","year":"2021","unstructured":"Li Y, Wen Z, Wang Y, et al. One-shot graph neural architecture search with dynamic search space. In: Proceedings of AAAI, 2021. 8510\u20138517"},{"key":"4539_CR19","first-page":"7968","volume-title":"Proceedings of ICML","author":"C Guan","year":"2022","unstructured":"Guan C, Wang X, Chen H, et al. Large-scale graph neural architecture search. In: Proceedings of ICML, 2022. 7968\u20137981"},{"key":"4539_CR20","first-page":"18083","volume-title":"Proceedings of ICML","author":"Y Qin","year":"2022","unstructured":"Qin Y, Wang X, Zhang Z, et al. Graph neural architecture search under distribution shifts. In: Proceedings of ICML, 2022. 18083\u201318095"},{"key":"4539_CR21","first-page":"611","volume-title":"Proceedings of the ACM Web Conference","author":"X Zheng","year":"2023","unstructured":"Zheng X, Zhang M, Chen C, et al. Auto-HEG: automated graph neural network on heterophilic graphs. In: Proceedings of the ACM Web Conference, 2023. 611\u2013620"},{"key":"4539_CR22","first-page":"24509","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"Z Wang","year":"2021","unstructured":"Wang Z, Di S, and Chen L. AutoGEL: an automated graph neural network with explicit link information. In: Proceedings of Advances in Neural Information Processing Systems, 2021. 24509\u201324522"},{"key":"4539_CR23","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1145\/3447548.3467447","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","author":"Y Ding","year":"2021","unstructured":"Ding Y, Yao Q, Zhao H, et al. DiffMG: differentiable meta graph search for heterogeneous graph neural networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021. 279\u2013288"},{"key":"4539_CR24","volume-title":"Proceedings of IEEE International Conference on Data Mining (ICDM)","author":"X Zheng","year":"2022","unstructured":"Zheng X, Zhang M, Chen C Y, et al. Multi-relational graph neural architecture search with fine-grained message passing. In: Proceedings of IEEE International Conference on Data Mining (ICDM), 2022"},{"key":"4539_CR25","first-page":"11307","volume-title":"Proceedings of 37th AAAI Conference on Artificial Intelligence, 35th Conference on Innovative Applications of Artificial Intelligence, 13th Symposium on Educational Advances in Artificial Intelligence","author":"Z Zhang","year":"2023","unstructured":"Zhang Z, Zhang Z, Wang X, et al. Dynamic heterogeneous graph attention neural architecture search. In: Proceedings of 37th AAAI Conference on Artificial Intelligence, 35th Conference on Innovative Applications of Artificial Intelligence, 13th Symposium on Educational Advances in Artificial Intelligence, Washington, 2023. 11307\u201311315"},{"key":"4539_CR26","first-page":"5195","volume-title":"Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","author":"X Zhang","year":"2024","unstructured":"Zhang X, Gao Y, Liu Y, et al. Meta structure search for link weight prediction in heterogeneous graphs. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024. 5195\u20135199"},{"key":"4539_CR27","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/978-3-030-63833-7_16","volume-title":"Proceedings of the 27th International Conference on Neural Information Processing","author":"Y Li","year":"2020","unstructured":"Li Y, King I. AutoGraph: automated graph neural network. In: Proceedings of the 27th International Conference on Neural Information Processing, Bangkok, 2020. 189\u2013201"},{"key":"4539_CR28","first-page":"8143","volume-title":"Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"B Xie","year":"2023","unstructured":"Xie B, Chang H, Zhang Z, et al. Adversarially robust neural architecture search for graph neural networks. In: Proceedings of IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, 2023. 8143\u20138152"},{"key":"4539_CR29","doi-asserted-by":"publisher","first-page":"692","DOI":"10.26599\/TST.2021.9010057","volume":"27","author":"B M Oloulade","year":"2022","unstructured":"Oloulade B M, Gao J, Chen J, et al. Graph neural architecture search: a survey. Tsinghua Sci Technol, 2022, 27: 692\u2013708","journal-title":"Tsinghua Sci Technol"},{"key":"4539_CR30","unstructured":"Achiam J, Adler S, Agarwal S, et al. GPT-4 technical report. 2023. ArXiv:2303.08774"},{"key":"4539_CR31","unstructured":"Guo J, Du L, Liu H. GPT4Graph: can large language models understand graph structured data? An empirical evaluation and benchmarking. 2023. ArXiv:2305.15066"},{"key":"4539_CR32","unstructured":"Zhang J. Graph-ToolFormer: to empower LLMs with graph reasoning ability via prompt augmented by ChatGPT. 2023. ArXiv:2304.11116"},{"key":"4539_CR33","first-page":"4171","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","author":"J Devlin","year":"2019","unstructured":"Devlin J, Chang M W, Lee K, et al. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Minneapolis, 2019. 4171\u20134186"},{"key":"4539_CR34","volume-title":"Proceedings of International Conference on Learning Representations","author":"Z Lan","year":"2020","unstructured":"Lan Z, Chen M, Goodman S, et al. Albert: a lite bert for self-supervised learning of language representations. In: Proceedings of International Conference on Learning Representations, 2020"},{"key":"4539_CR35","volume-title":"Proceedings of ICLR","author":"P He","year":"2021","unstructured":"He P, Liu X, Gao J, et al. Deberta: decoding-enhanced bert with disentangled attention. In: Proceedings of ICLR, 2021"},{"key":"4539_CR36","unstructured":"Chowdhery A, Narang S, Devlin J, et al. PaLM: scaling language modeling with pathways. 2022. ArXiv:2204.02311"},{"key":"4539_CR37","first-page":"5998","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"A Vaswani","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, 2017. 5998\u20136008"},{"key":"4539_CR38","unstructured":"Anil R, Dai A M, Firat O, et al. PaLM 2 technical report. 2023. ArXiv:2305.10403"},{"key":"4539_CR39","unstructured":"Li Y, Li Z, Wang P, et al. A survey of graph meets large language model: progress and future directions. 2023. ArXiv:2311.12399"},{"key":"4539_CR40","unstructured":"He X, Bresson X, Laurent T, et al. Harnessing explanations: LLM-to-LM interpreter for enhanced text-attributed graph representation learning. 2023. ArXiv:2305.19523"},{"key":"4539_CR41","doi-asserted-by":"crossref","unstructured":"Tang J, Yang Y, Wei W, et al. GraphGPT: graph instruction tuning for large language models. 2024. ArXiv:2310.13023","DOI":"10.1145\/3626772.3657775"},{"key":"4539_CR42","first-page":"481","volume-title":"Proceedings of the ACM on Web Conference","author":"Z Liu","year":"2024","unstructured":"Liu Z, He X, Tian Y, et al. Can we soft prompt LLMs for graph learning tasks? In: Proceedings of the ACM on Web Conference, Singapore, 2024. 481\u2013484"},{"key":"4539_CR43","unstructured":"Zhang S, Gong C, Wu L, et al. AutoML-GPT: automatic machine learning with GPT. 2023. ArXiv:2305.02499"},{"key":"4539_CR44","volume-title":"Proceedings of NeurIPS","author":"Y Qin","year":"2022","unstructured":"Qin Y, Zhang Z, Wang X, et al. NAS-Bench-Graph: benchmarking graph neural architecture search. In: Proceedings of NeurIPS, 2022"},{"key":"4539_CR45","doi-asserted-by":"crossref","unstructured":"Zhang Z, Wang X, Zhang Z, et al. LLM4DyG: can large language models solve problems on dynamic graphs? 2023. ArXiv:2310.17110","DOI":"10.1145\/3637528.3671709"},{"key":"4539_CR46","unstructured":"Huang J, Zhang X, Mei Q, et al. Can LLMs effectively leverage graph structural information: when and why. 2023. ArXiv:2309.16595"},{"key":"4539_CR47","first-page":"36","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"H Wang","year":"2024","unstructured":"Wang H, Feng S, He T, et al. Can language models solve graph problems in natural language? In: Proceedings of Advances in Neural Information Processing Systems, 2024. 36"},{"key":"4539_CR48","doi-asserted-by":"publisher","first-page":"57","DOI":"10.18653\/v1\/W15-4007","volume-title":"Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality","author":"K Toutanova","year":"2015","unstructured":"Toutanova K, Chen D. Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd Workshop on Continuous Vector Space Models and Their Compositionality, 2015. 57\u201366"},{"key":"4539_CR49","first-page":"1811","volume-title":"Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence","author":"T Dettmers","year":"2018","unstructured":"Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2D knowledge graph embeddings. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, the 30th Innovative Applications of Artificial Intelligence, and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence, 2018. 1811\u20131818"},{"key":"4539_CR50","unstructured":"Zeng A, Liu X, Du Z, et al. GLM-130B: an open bilingual pre-trained model. 2022. ArXiv:2210.02414"},{"key":"4539_CR51","first-page":"1877","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"T Brown","year":"2020","unstructured":"Brown T, Mann B, Ryder N, et al. Language models are few-shot learners. In: Proceedings of Advances in Neural Information Processing Systems, 2020. 33: 1877\u20131901"},{"key":"4539_CR52","first-page":"22118","volume-title":"Proceedings of Advances in Neural Information Processing Systems","author":"W Hu","year":"2020","unstructured":"Hu W, Fey M, Zitnik M, et al. Open graph benchmark: datasets for machine learning on graphs. In: Proceedings of Advances in Neural Information Processing Systems, 2020. 33: 22118\u201322133"},{"key":"4539_CR53","first-page":"1817","volume-title":"Proceedings of ACM Web Conference","author":"W Zhang","year":"2022","unstructured":"Zhang W, Shen Y, Lin Z, et al. PaSca: a graph neural architecture search system under the scalable paradigm. In: Proceedings of ACM Web Conference, 2022. 1817\u20131828"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-024-4539-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-024-4539-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-024-4539-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,23]],"date-time":"2025-10-23T04:35:57Z","timestamp":1761194157000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-024-4539-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,20]]},"references-count":53,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["4539"],"URL":"https:\/\/doi.org\/10.1007\/s11432-024-4539-1","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,20]]},"assertion":[{"value":"22 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 October 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"222103"}}