{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T02:01:57Z","timestamp":1777341717177,"version":"3.51.4"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031219665","type":"print"},{"value":"9783031219672","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21967-2_11","type":"book-chapter","created":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T08:02:35Z","timestamp":1670486555000},"page":"128-140","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Graph Neural Networks-Based Multilabel Classification of\u00a0Citation Network"],"prefix":"10.1007","author":[{"given":"Guillaume","family":"Lachaud","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Patricia","family":"Conde-Cespedes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Trocan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,9]]},"reference":[{"key":"11_CR1","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015 Conference Track Proceedings (2015)"},{"key":"11_CR2","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, pp. 3844\u20133852. NIPS 2016, Curran Associates Inc., Red Hook, NY, USA (2016)"},{"key":"11_CR3","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"11_CR4","unstructured":"Dwivedi, V.P., Joshi, C.K., Laurent, T., Bengio, Y., Bresson, X.: Benchmarking graph neural networks. arXiv:2003.00982 [cs, stat] (2020)"},{"key":"11_CR5","unstructured":"Fey, M., Lenssen, J.E.: Fast graph representation learning with pytorch geometric. arXiv:1903.02428 [cs, stat] (2019)"},{"key":"11_CR6","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: Precup, D., Teh, Y.W. (eds.) Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6\u201311 August 2017. Proceedings of Machine Learning Research, vol. 70, pp. 1263\u20131272. PMLR (2017)"},{"key":"11_CR7","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 1025\u20131035. NIPS 2017, Curran Associates Inc., Red Hook, NY, USA (2017)"},{"issue":"2","key":"11_CR8","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.acha.2010.04.005","volume":"30","author":"DK Hammond","year":"2011","unstructured":"Hammond, D.K., Vandergheynst, P., Gribonval, R.: Wavelets on graphs via spectral graph theory. Appl. Comput. Harmonic Anal. 30(2), 129\u2013150 (2011). https:\/\/doi.org\/10.1016\/j.acha.2010.04.005","journal-title":"Appl. Comput. Harmonic Anal."},{"key":"11_CR9","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H.T. (eds.) Advances in Neural Information Processing Systems, vol. 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, 6\u201312 December 2020, Virtual (2020)"},{"key":"11_CR10","unstructured":"Huang, Q., He, H., Singh, A., Lim, S.N., Benson, A.R.: Combining label propagation and simple models out-performs graph neural networks. In: 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, 3\u20137 May 2021. OpenReview.net (2021)"},{"key":"11_CR11","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24\u201326 April 2017, Conference Track Proceedings. OpenReview.net (2017)"},{"issue":"6","key":"11_CR12","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017). https:\/\/doi.org\/10.1145\/3065386","journal-title":"Commun. ACM"},{"key":"11_CR13","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1007\/978-3-030-46147-8_9","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"J Lanchantin","year":"2020","unstructured":"Lanchantin, J., Sekhon, A., Qi, Y.: Neural message passing for multi-label classification. In: Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., Robardet, C. (eds.) ECML PKDD 2019. LNCS (LNAI), vol. 11907, pp. 138\u2013163. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-46147-8_9"},{"key":"11_CR14","doi-asserted-by":"publisher","unstructured":"Li, G., Muller, M., Thabet, A., Ghanem, B.: DeepGCNs: can GCNs go as deep as CNNs? In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 9266\u20139275. IEEE, Seoul, Korea (South) (2019). https:\/\/doi.org\/10.1109\/ICCV.2019.00936","DOI":"10.1109\/ICCV.2019.00936"},{"key":"11_CR15","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Bengio, Y., LeCun, Y. (eds.) 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, 2\u20134 May 2013, Workshop Track Proceedings (2013)"},{"key":"11_CR16","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/978-3-662-44851-9_28","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"J Nam","year":"2014","unstructured":"Nam, J., Kim, J., Loza Menc\u00eda, E., Gurevych, I., F\u00fcrnkranz, J.: Large-scale multi-label text classification \u2014 revisiting neural networks. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 437\u2013452. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44851-9_28"},{"issue":"3","key":"11_CR17","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1609\/aimag.v29i3.2157","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93 (2008). https:\/\/doi.org\/10.1609\/aimag.v29i3.2157","journal-title":"AI Mag."},{"issue":"3","key":"11_CR18","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1109\/72.572108","volume":"8","author":"A Sperduti","year":"1997","unstructured":"Sperduti, A., Starita, A.: Supervised neural networks for the classification of structures. IEEE Trans. Neural Netw. 8(3), 714\u2013735 (1997). https:\/\/doi.org\/10.1109\/72.572108","journal-title":"IEEE Trans. Neural Netw."},{"key":"11_CR19","unstructured":"Sun, C., Wu, G.: Adaptive graph diffusion networks with hop-wise attention. arXiv:2012.15024 [cs] (2020)"},{"key":"11_CR20","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, 30 April \u20133 May 2018, Conference Track Proceedings. OpenReview.net (2018)"},{"key":"11_CR21","doi-asserted-by":"publisher","unstructured":"Wang, J., Yang, Y., Mao, J., Huang, Z., Huang, C., Xu, W.: CNN-RNN: a unified framework for multi-label image classification. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2285\u20132294. IEEE, Las Vegas, NV, USA (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.251","DOI":"10.1109\/CVPR.2016.251"},{"issue":"1","key":"11_CR22","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1162\/qss\\_a_00021","volume":"1","author":"K Wang","year":"2020","unstructured":"Wang, K., Shen, Z., Huang, C., Wu, C.H., Dong, Y., Kanakia, A.: Microsoft academic graph: when experts are not enough. Quant. Sci. Stud. 1(1), 396\u2013413 (2020). https:\/\/doi.org\/10.1162\/qss_a_00021","journal-title":"Quant. Sci. Stud."},{"issue":"1","key":"11_CR23","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","volume":"32","author":"Z Wu","year":"2021","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Yu, P.S.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.2978386","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"1","key":"11_CR24","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1109\/TKDE.2020.2981333","volume":"34","author":"Z Zhang","year":"2022","unstructured":"Zhang, Z., Cui, P., Zhu, W.: Deep learning on graphs: a survey. IEEE Trans. Knowl. Data Eng. 34(1), 249\u2013270 (2022). https:\/\/doi.org\/10.1109\/TKDE.2020.2981333","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Lecture Notes in Computer Science","Intelligent Information and Database Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21967-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,8]],"date-time":"2022-12-08T08:12:47Z","timestamp":1670487167000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21967-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031219665","9783031219672"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21967-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"9 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACIIDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Intelligent Information and Database Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ho Chi Minh City","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vietnam","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aciids2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aciids.pwr.edu.pl\/2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}