{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T20:47:38Z","timestamp":1774385258432,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"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":["World Wide Web"],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s11280-023-01179-7","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T10:02:21Z","timestamp":1690452141000},"page":"3389-3408","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Structure-adaptive graph neural network with temporal representation and residual connections"],"prefix":"10.1007","volume":"26","author":[{"given":"Xin","family":"Bi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingling","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixun","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Yao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haojie","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George Y.","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangguo","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjiao","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"issue":"2","key":"1179_CR1","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1007\/s12559-018-9614-5","volume":"11","author":"F Shi","year":"2019","unstructured":"Shi, F., Dey, N., Ashour, A.S., Sifaki-Pistolla, D., Sherratt, R.S.: Metakansei modeling with valence-arousal fmri dataset of brain. Cogn. Comput. 11(2), 227\u2013240 (2019)","journal-title":"Cogn. Comput."},{"issue":"6","key":"1179_CR2","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/nrn1119","volume":"4","author":"D Le Bihan","year":"2003","unstructured":"Le Bihan, D.: Looking into the functional architecture of the brain with diffusion mri. Nat. Rev. Neurosci. 4(6), 469\u2013480 (2003)","journal-title":"Nat. Rev. Neurosci."},{"issue":"1\u20132","key":"1179_CR3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1002\/hbm.460020107","volume":"2","author":"KJ Friston","year":"1994","unstructured":"Friston, K.J.: Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain. Map. 2(1\u20132), 56\u201378 (1994)","journal-title":"Hum. Brain. Map."},{"key":"1179_CR4","doi-asserted-by":"publisher","first-page":"110036","DOI":"10.1016\/j.knosys.2022.110036","volume":"258","author":"X Song","year":"2022","unstructured":"Song, X., Li, J., Cai, T., Yang, S., Yang, T., Liu, C.: A survey on deep learning based knowledge tracing. Knowledge-Based Systems 258, 110036 (2022)","journal-title":"Knowledge-Based Systems"},{"key":"1179_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2023.01.131","volume":"629","author":"J Liu","year":"2023","unstructured":"Liu, J., Chen, Y., Huang, X., Li, J., Min, G.: Gnn-based long and short term preference modeling for next-location prediction. Inform Sci 629, 1\u201314 (2023)","journal-title":"Inform Sci"},{"key":"1179_CR6","doi-asserted-by":"publisher","first-page":"1456","DOI":"10.1109\/TII.2022.3206343","volume":"19","author":"C Xu","year":"2022","unstructured":"Xu, C., Zhao, W., Zhao, J., Guan, Z., Song, X., Li, J.: Uncertaintyaware multiview deep learning for internet of things applications. IEEE Trans. Indust. Inform. 19, 1456\u20131466 (2022)","journal-title":"IEEE Trans. Indust. Inform."},{"issue":"3","key":"1179_CR7","doi-asserted-by":"publisher","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059\u20131069 (2010)","journal-title":"Neuroimage"},{"issue":"6","key":"1179_CR8","doi-asserted-by":"publisher","first-page":"6835","DOI":"10.1007\/s10489-021-02668-w","volume":"52","author":"J Ji","year":"2022","unstructured":"Ji, J., Yao, Y.: A novel cnn framework to extract multi-level modular features for the classification of brain networks. Appl. Intell. 52(6), 6835\u20136852 (2022)","journal-title":"Appl. Intell."},{"key":"1179_CR9","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Guerrero, R., Glocker, B., Rueckert, D.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer\u2019s disease. Med. Image Anal. 48, 117\u2013130 (2018)","journal-title":"Med. Image Anal."},{"issue":"11","key":"1179_CR10","doi-asserted-by":"publisher","first-page":"876","DOI":"10.14778\/3402707.3402726","volume":"4","author":"Y Yuan","year":"2011","unstructured":"Yuan, Y., Wang, G., Wang, H., Chen, L.: Efficient subgraph search over large uncertain graphs. Proc VLDB Endow 4(11), 876\u2013886 (2011)","journal-title":"Proc VLDB Endow"},{"key":"1179_CR11","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","volume":"48","author":"S Parisot","year":"2018","unstructured":"Parisot, S., Ktena, S.I., Ferrante, E., Lee, M., Guerrero, R., Glocker, B., Rueckert, D.: Disease prediction using graph convolutional networks: application to autism spectrum disorder and alzheimer\u2019s disease. Med Image Anal 48, 117\u2013130 (2018)","journal-title":"Med Image Anal"},{"key":"1179_CR12","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, L.,Wang, G.: Efficiently answering probability thresholdbased shortest path queries over uncertain graphs. In: Database Systems for Advanced Applications: 15th International Conference, DASFAA 2010, Tsukuba, Japan, April 1-4, 2010, Proceedings, Part I 15, 155\u2013170. Springer (2010)","DOI":"10.1007\/978-3-642-12026-8_14"},{"key":"1179_CR13","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Wang, G., Chen, L., Wang, H.: Efficient subgraph similarity search on large probabilistic graph databases. arXiv:1205.6692 (2012)","DOI":"10.14778\/2311906.2311908"},{"key":"1179_CR14","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Huang, H.: New graph-blind convolutional network for brain connectome data analysis. In: International Conference on Information Processing in Medical Imaging, 669\u2013681. Springer (2019)","DOI":"10.1007\/978-3-030-20351-1_52"},{"key":"1179_CR15","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.jpsychires.2017.04.007","volume":"92","author":"J Brakowski","year":"2017","unstructured":"Brakowski, J., Spinelli, S., D\u00f6rig, N., Bosch, O.G., Manoliu, A., Holtforth, M.G., Seifritz, E.: Resting state brain network function in major depression-depression symptomatology, antidepressant treatment effects, future research. J. Psychiatric Res. 92, 147\u2013159 (2017)","journal-title":"J. Psychiatric Res."},{"issue":"4","key":"1179_CR16","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1371\/journal.pcbi.0010042","volume":"1","author":"O Sporns","year":"2005","unstructured":"Sporns, O., Tononi, G., K\u00f6tter, R.: The human connectome: a structural description of the human brain. PLoS Comput. Biol. 1(4), 42 (2005)","journal-title":"PLoS Comput. Biol."},{"issue":"1","key":"1179_CR17","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.bbr.2008.08.012","volume":"197","author":"H-Y Zhang","year":"2009","unstructured":"Zhang, H.-Y., Wang, S.-J., Xing, J., Liu, B., Ma, Z.-L., Yang, M., Zhang, Z.-J., Teng, G.-J.: Detection of pcc functional connectivity characteristics in resting-state fmri in mild alzheimer\u2019s disease. Behav. Brain Res. 197(1), 103\u2013108 (2009)","journal-title":"Behav. Brain Res."},{"issue":"6","key":"1179_CR18","doi-asserted-by":"publisher","first-page":"541","DOI":"10.2174\/156720509790147106","volume":"6","author":"C Sorg","year":"2009","unstructured":"Sorg, C., Riedl, V., Perneczky, R., Kurz, A., Wohlschlager, A.M.: Impact of alzheimer\u2019s disease on the functional connectivity of spontaneous brain activity. Curr. Alzheimer Res. 6(6), 541\u2013553 (2009)","journal-title":"Curr. Alzheimer Res."},{"issue":"1457","key":"1179_CR19","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1098\/rstb.2005.1634","volume":"360","author":"CF Beckmann","year":"2005","unstructured":"Beckmann, C.F., DeLuca, M., Devlin, J.T., Smith, S.M.: Investigations into resting-state connectivity using independent component analysis. Phil. Transac. Royal Soc. B Biol. Sci. 360(1457), 1001\u20131013 (2005)","journal-title":"Phil. Transac. Royal Soc. B Biol. Sci."},{"key":"1179_CR20","doi-asserted-by":"publisher","first-page":"106746","DOI":"10.1016\/j.knosys.2021.106746","volume":"214","author":"S Min","year":"2021","unstructured":"Min, S., Gao, Z., Peng, J., Wang, L., Qin, K., Fang, B.: Stgsn - spatial-temporal graph neural network framework for time-evolving social networks. Knowledge-Based Systems 214, 106746 (2021)","journal-title":"Knowledge-Based Systems"},{"issue":"Supplement 1","key":"1179_CR21","doi-asserted-by":"publisher","first-page":"262","DOI":"10.1093\/bioinformatics\/btab270","volume":"37","author":"R You","year":"2021","unstructured":"You, R., Yao, S., Mamitsuka, H., Zhu, S.: Deepgraphgo: graph neural network for large-scale, multispecies protein function prediction. Bioinformatics 37(Supplement 1), 262\u2013271 (2021)","journal-title":"Bioinformatics"},{"issue":"2","key":"1179_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103242","volume":"60","author":"X Bi","year":"2023","unstructured":"Bi, X., Nie, H., Zhang, G., Hu, L., Ma, Y., Zhao, X., Yuan, Y., Wang, G.: Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision. Inform. Proc. Manag. 60(2), 103242 (2023)","journal-title":"Inform. Proc. Manag."},{"key":"1179_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2023.108660","volume":"108","author":"Y Qi","year":"2023","unstructured":"Qi, Y., Gu, Z., Li, A., Zhang, X., Shafiq, M., Mei, Y., Lin, K.: Cybersecurity knowledge graph enabled attack chain detection for cyber-physical systems. Computers and Electrical Engineering 108, 108660 (2023)","journal-title":"Computers and Electrical Engineering"},{"key":"1179_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.103242","volume":"60","author":"X Bi","year":"2023","unstructured":"Bi, X., Nie, H., Zhang, G., Hu, L., Ma, Y., Zhao, X., Yuan, Y., Wang, G.: Boosting question answering over knowledge graph with reward integration and policy evaluation under weak supervision. Inform. Proc. Manag. 60, 103242 (2023)","journal-title":"Inform. Proc. Manag."},{"key":"1179_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.110036","volume":"258","author":"X Song","year":"2022","unstructured":"Song, X., Li, J., Cai, T., Yang, S., Yang, T., Liu, C.: A survey on deep learning based knowledge tracing. Knowledge-Based Systems 258, 110036 (2022)","journal-title":"Knowledge-Based Systems"},{"key":"1179_CR26","doi-asserted-by":"crossref","unstructured":"Jia, Y., Lin, M., Wang, Y., Li, J., Chen, K., Siebert, J., Zhang, G.Z., Liao, Q.: Extrapolation over temporal knowledge graph via hyperbolic embedding. CAAI Transactions on Intelligence Technology (2023)","DOI":"10.1049\/cit2.12186"},{"issue":"2","key":"1179_CR27","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.67.026223","volume":"67","author":"D He","year":"2003","unstructured":"He, D., Zheng, Z., Stone, L.: Detecting generalized synchrony: An improved approach. Physical Review E 67(2), 026223 (2003)","journal-title":"Physical Review E"},{"key":"1179_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109852","volume":"257","author":"S Yang","year":"2022","unstructured":"Yang, S., Cai, B., Cai, T., Song, X., Jiang, J., Li, B., Li, J.: Robust crossnetwork node classification via constrained graph mutual information. Knowledge-Based Systems 257, 109852 (2022)","journal-title":"Knowledge-Based Systems"},{"key":"1179_CR29","doi-asserted-by":"crossref","unstructured":"Fang, U., Li, J., Lu, X., Mian, A., Gu, Z.: Robust image clustering via context-aware contrastive graph learning. Pattern Recog. 109340 (2023)","DOI":"10.1016\/j.patcog.2023.109340"},{"issue":"12","key":"1179_CR30","doi-asserted-by":"publisher","first-page":"2767","DOI":"10.1109\/TKDE.2012.222","volume":"25","author":"Y Yuan","year":"2013","unstructured":"Yuan, Y., Wang, G., Chen, L., Wang, H.: Efficient keyword search on uncertain graph data. IEEE Transactions on Knowledge and Data Engineering 25(12), 2767\u20132779 (2013)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"1179_CR31","doi-asserted-by":"crossref","unstructured":"Fang, U., Li, J., Akhtar, N., Li, M., Jia, Y.: Gomic: Multi-view image clustering via self-supervised contrastive heterogeneous graph co-learning. World Wide Web, 1\u201317 (2022)","DOI":"10.21203\/rs.3.rs-1904975\/v2"},{"key":"1179_CR32","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Zhu, F., Huang, J.: Adaptive graph convolutional neural networks. AAAI Conference on Artificial Intelligence. 3546\u20133553 (2018)","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"1179_CR33","unstructured":"Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA. 1024\u20131034 (2017)"},{"key":"1179_CR34","doi-asserted-by":"crossref","unstructured":"Xing, X., Li, Q., Wei, H., Zhang, M., Zhan, Y., Zhou, X.S., Xue, Z., Shi, F.: Dynamic spectral graph convolution networks with assistant task training for early mci diagnosis. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part IV, 639\u2013646 Springer. (2019)","DOI":"10.1007\/978-3-030-32251-9_70"},{"key":"1179_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, H., Song, R., Wang, L., Zhang, L., Wang, D., Wang, C., Zhang, W.: Classification of brain disorders in rs-fmri via local-to-global graph neural networks. IEEE Transactions on Medical Imaging (2022)","DOI":"10.1109\/TMI.2022.3219260"},{"key":"1179_CR36","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Cui, H., He, L., Sun, L., Yang, C.: Joint embedding of structural and functional brain networks with graph neural networks for mental illness diagnosis. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 272\u2013276 IEEE. (2022)","DOI":"10.1109\/EMBC48229.2022.9871118"},{"key":"1179_CR37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"1179_CR38","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"1179_CR39","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, S., Zhu, F., Huang, J.: Adaptive graph convolutional neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11691"},{"key":"1179_CR40","doi-asserted-by":"crossref","unstructured":"Zhuang, C., Ma, Q.: Dual graph convolutional networks for graph-based semi-supervised classification. In: Proceedings of the 2018 World Wide Web Conference, 499\u2013508 (2018)","DOI":"10.1145\/3178876.3186116"},{"key":"1179_CR41","unstructured":"Yun, S., Jeong, M., Kim, R., Kang, J., Kim, H.J.: Graph transformer networks. Advances in neural information processing systems 32, 11983\u201311993 (2019)"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-023-01179-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-023-01179-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-023-01179-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,11]],"date-time":"2023-10-11T04:26:47Z","timestamp":1696998407000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-023-01179-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,27]]},"references-count":41,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["1179"],"URL":"https:\/\/doi.org\/10.1007\/s11280-023-01179-7","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"value":"1386-145X","type":"print"},{"value":"1573-1413","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,27]]},"assertion":[{"value":"19 March 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2023","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 conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"This article does not contain any studies involving human participants and\/or animals by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics standard"}},{"value":"Informed consent was obtained from all individual participants.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}}]}}