{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:15:00Z","timestamp":1742912100332,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819608461"},{"type":"electronic","value":"9789819608478"}],"license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-0847-8_22","type":"book-chapter","created":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T04:27:12Z","timestamp":1734064032000},"page":"315-327","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of BERT-GraphSAGE Model in Text and Paper Classification Tasks"],"prefix":"10.1007","author":[{"given":"Junwen","family":"Lu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingrui","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Moudong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"key":"22_CR1","unstructured":"Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B.: Open graph benchmark: datasets for machine learning on graphs. In: Advances in Neural Information Processing Systems, vol. 33, pp. 22118\u201322133 (2020)"},{"issue":"1","key":"22_CR2","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1186\/s40537-022-00656-6","volume":"9","author":"MM Abdelgwad","year":"2022","unstructured":"Abdelgwad, M.M., Soliman, T.H.A., Taloba, A.I.: Arabic aspect sentiment polarity classification using BERT. J. Big Data 9(1), 115 (2022)","journal-title":"J. Big Data"},{"key":"22_CR3","doi-asserted-by":"publisher","first-page":"116463","DOI":"10.1016\/j.eswa.2021.116463","volume":"193","author":"R Van Belle","year":"2022","unstructured":"Van Belle, R., Van Damme, C., Tytgat, H., De Weerdt, J.: Inductive graph representation learning for fraud detection. Expert Syst. Appl. 193, 116463 (2022)","journal-title":"Expert Syst. Appl."},{"issue":"1","key":"22_CR4","doi-asserted-by":"publisher","first-page":"145067","DOI":"10.1109\/ACCESS.2023.3345795","volume":"11","author":"Y Li","year":"2023","unstructured":"Li, Y., Xue, C., Zargari, F., Li, Y.: From graph theory to graph neural networks (GNNs): the opportunities of GNNs in power electronics. IEEE Access 11(1), 145067\u2013145084 (2023)","journal-title":"IEEE Access"},{"issue":"1","key":"22_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, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. 32(1), 4\u201324 (2020)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"22_CR6","unstructured":"Dehmamy, N., Barab\u00e1si, A.L., Yu, R.: Understanding the representation power of graph neural networks in learning graph topology. In: Advances in Neural Information Processing Systems, vol. 32, pp. 1320\u20131331 (2019)"},{"issue":"1","key":"22_CR7","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, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"22_CR8","unstructured":"Ye, Z., Jiang, G., Liu, Y., Li, Z., Yuan, J.: Document and word representations generated by graph convolutional network and BERT for short text classification. In: 24th European Conference on Artificial Intelligence, pp. 2275\u20132281. IOS Press, Amsterdam (2020)"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Yu, Q., Wang, Z., Jiang, K.: Research on text classification based on BERT-BiGRU model. In: Journal of Physics: Conference Series, vol. 1746, no. 1, p. 012019 (2021)","DOI":"10.1088\/1742-6596\/1746\/1\/012019"},{"issue":"3","key":"22_CR10","first-page":"2544","volume":"35","author":"Z Zhang","year":"2021","unstructured":"Zhang, Z., Cui, P., Pei, J., Wang, X., Zhu, W.: Eigen-GNN: a graph structure preserving plug-in for GNNs. IEEE Trans. Knowl. Data Eng. 35(3), 2544\u20132555 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Guo, J., Zhang, Z., Xu, L., Chen, B., Chen, E.: Adaptive Adapters: an efficient way to incorporate BERT into neural machine translation. In: IEEE\/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1740\u20131751 (2021)","DOI":"10.1109\/TASLP.2021.3076863"},{"issue":"3","key":"22_CR12","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1109\/JSAC.2022.3229422","volume":"41","author":"L Zeng","year":"2022","unstructured":"Zeng, L., Yang, C., Huang, P., Zhou, Z., Yu, S., Chen, X.: GNN at the edge: cost-efficient graph neural network processing over distributed edge servers. IEEE J. Sel. Areas Commun. 41(3), 720\u2013739 (2022)","journal-title":"IEEE J. Sel. Areas Commun."},{"issue":"8","key":"22_CR13","doi-asserted-by":"publisher","first-page":"13084","DOI":"10.1109\/TITS.2021.3119638","volume":"23","author":"D Xu","year":"2021","unstructured":"Xu, D., Peng, H., Wei, C., Shang, X., Li, H.: Traffic state data imputation: an efficient generating method based on the graph aggregator. IEEE Trans. Intell. Transp. Syst. 23(8), 13084\u201313093 (2021)","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"issue":"4","key":"22_CR14","doi-asserted-by":"publisher","first-page":"4593","DOI":"10.1109\/TNNLS.2022.3144343","volume":"35","author":"X Ai","year":"2022","unstructured":"Ai, X., Sun, C., Zhang, Z., Hancock, E.R.: Two-level graph neural network. IEEE Trans. Neural Netw. Learn. Syst. 35(4), 4593\u20134606 (2022)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"11","key":"22_CR15","first-page":"13454","volume":"45","author":"B Wang","year":"2023","unstructured":"Wang, B., Jiang, B., Tang, J., Luo, B.: Generalizing aggregation functions in GNNs: building high capacity and robust GNNs via nonlinear aggregation. IEEE Trans. Pattern Anal. Mach. Intell. 45(11), 13454\u201313466 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Lin, Y., et al.: Bertgcn: transductive text classification by combining gcn and bert.\u00a0arxiv preprint arxiv:2105.05727 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.126"},{"key":"22_CR17","doi-asserted-by":"crossref","unstructured":"Le, N.Q.K., Ho, Q.T., Nguyen, T.T.D., Ou, Y.Y.: A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information. Briefings Bioinf. 22(5), bbab005 (2021)","DOI":"10.1093\/bib\/bbab005"},{"issue":"12","key":"22_CR18","first-page":"3127","volume":"71","author":"Y Han","year":"2022","unstructured":"Han, Y., Park, K., Jung, Y., Kim, L.S.: EGCN: an efficient GCN accelerator for minimizing off-chip memory access. IEEE Trans. Comput. 71(12), 3127\u20133139 (2022)","journal-title":"IEEE Trans. Comput."},{"issue":"1","key":"22_CR19","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s40537-023-00710-x","volume":"10","author":"H Zou","year":"2023","unstructured":"Zou, H., Wang, Z.: A semi-supervised short text sentiment classification method based on improved bert model from unlabelled data. J. Big Data 10(1), 35 (2023)","journal-title":"J. Big Data"},{"issue":"1","key":"22_CR20","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1109\/TNNLS.2021.3094987","volume":"34","author":"Y Zhou","year":"2021","unstructured":"Zhou, Y., Liao, L., Gao, Y., Wang, R., Huang, H.: TopicBERT: a topic-enhanced neural language model fine-tuned for sentiment classification. IEEE Trans. Neural Netw. Learn. Syst. 34(1), 380\u2013393 (2021)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"10","key":"22_CR21","doi-asserted-by":"publisher","first-page":"6169","DOI":"10.1109\/TPAMI.2021.3085738","volume":"44","author":"Y Li","year":"2021","unstructured":"Li, Y., Cui, B., Zhang, Z.: Efficient relational sentence ordering network. IEEE Trans. Pattern Anal. Mach. Intell. 44(10), 6169\u20136183 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1","key":"22_CR22","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/LCA.2022.3168067","volume":"21","author":"H Lin","year":"2022","unstructured":"Lin, H., et al.: Characterizing and understanding distributed GNN training on GPUs. IEEE Comput. Archit. Lett. 21(1), 21\u201324 (2022)","journal-title":"IEEE Comput. Archit. Lett."},{"key":"22_CR23","unstructured":"Song, X., Ma, R., Li, J., Zhang, M., Wipf, D.P.: Network in graph neural network. arXiv preprint arXiv:2111.11638 (2021)"},{"issue":"14","key":"22_CR24","doi-asserted-by":"publisher","first-page":"11679","DOI":"10.1007\/s00521-022-07059-x","volume":"34","author":"D El Alaoui","year":"2022","unstructured":"El Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., Tairi, H.: Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network. Neural Comput. Appl. 34(14), 11679\u201311690 (2022)","journal-title":"Neural Comput. Appl."},{"key":"22_CR25","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhu, T., Zhu, J., Li, T., Wang, C.: A optimized BERT for multimodal sentiment analysis. ACM Trans. Multimedia Comput. Commun. Appl. 19(2s), 12 (2023)","DOI":"10.1145\/3566126"},{"issue":"5","key":"22_CR26","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MIS.2020.2998040","volume":"35","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., et al.: Personalized geographical influence modeling for POI recommendation. IEEE Intell. Syst. 35(5), 18\u201327 (2020)","journal-title":"IEEE Intell. Syst."},{"issue":"1","key":"22_CR27","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1109\/TKDE.2016.2611577","volume":"29","author":"B Gu","year":"2016","unstructured":"Gu, B., et al.: The interaction between schema matching and record matching in data integration. IEEE Trans. Knowl. Data Eng. 29(1), 186\u2013199 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"22_CR28","doi-asserted-by":"crossref","unstructured":"Subakti, A., Murfi, H., Hariadi, N.: The performance of BERT as data representation of text clustering.\u00a0J. Big Data\u00a09, 15 (2022)","DOI":"10.1186\/s40537-022-00564-9"},{"issue":"1","key":"22_CR29","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1007\/s11280-018-0638-2","volume":"22","author":"D Zhai","year":"2019","unstructured":"Zhai, D., et al.: Towards secure and truthful task assignment in spatial crowdsourcing. World Wide Web 22(1), 2017\u20132040 (2019)","journal-title":"World Wide Web"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Xu, R., Liu, G., Wang, Y., Zhang, X., Zheng, K., Zhou, X.: Adaptive hypergraph network for trust prediction. In: Proceedings of the 40th IEEE International Conference on Data Engineering, pp. 2986\u20132999 (2024)","DOI":"10.1109\/ICDE60146.2024.00232"},{"issue":"1","key":"22_CR31","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1007\/s40747-022-00819-1","volume":"9","author":"C Liu","year":"2023","unstructured":"Liu, C., Zhu, W., Zhang, X., Zhai, Q.: Sentence part-enhanced BERT with respect to downstream tasks. Complex Intell. Syst. 9(1), 463\u2013474 (2023)","journal-title":"Complex Intell. Syst."}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-0847-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,13]],"date-time":"2024-12-13T05:06:03Z","timestamp":1734066363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-0847-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,14]]},"ISBN":["9789819608461","9789819608478"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-0847-8_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,14]]},"assertion":[{"value":"14 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sydney, NSW","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2024.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}