{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T16:57:59Z","timestamp":1780160279373,"version":"3.54.0"},"publisher-location":"New York, NY, USA","reference-count":37,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T00:00:00Z","timestamp":1698278400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Guangxi Natural Science Foundation","award":["2023GXNSFBA026010"],"award-info":[{"award-number":["2023GXNSFBA026010"]}]},{"name":"National Key Research and Development Program of China under Grant","award":["2022YFA1004100"],"award-info":[{"award-number":["2022YFA1004100"]}]},{"name":"Guangxi ?Bagui? Teams for Innovation and Research, China"},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security","award":["22-A-03-02"],"award-info":[{"award-number":["22-A-03-02"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62166003"],"award-info":[{"award-number":["No. 62166003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,26]]},"DOI":"10.1145\/3581783.3613117","type":"proceedings-article","created":{"date-parts":[[2023,10,27]],"date-time":"2023-10-27T07:27:30Z","timestamp":1698391650000},"page":"2946-2954","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Multi-teacher Self-training for Semi-supervised Node Classification with Noisy Labels"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9229-5188","authenticated-orcid":false,"given":"Yujing","family":"Liu","sequence":"first","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4687-3475","authenticated-orcid":false,"given":"Zongqian","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1150-6181","authenticated-orcid":false,"given":"Zhengyu","family":"Lu","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4757-3557","authenticated-orcid":false,"given":"Guoqiu","family":"Wen","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5859-8389","authenticated-orcid":false,"given":"Junbo","family":"Ma","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-6269","authenticated-orcid":false,"given":"Guangquan","family":"Lu","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6840-0578","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining & Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"233","volume-title":"PMLR","author":"Arpit D.","unstructured":"Arpit, D., Jastrz\u0119bski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A., Bengio, Y., et al. A closer look at memorization in deep networks. In ICML (2017), PMLR, pp. 233--242."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/279943.279962"},{"key":"e_1_3_2_1_3_1","volume-title":"Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815","author":"Bojchevski A.","year":"2017","unstructured":"Bojchevski, A., and G\u00fcnnemann, S. Deep gaussian embedding of graphs: Unsupervised inductive learning via ranking. arXiv preprint arXiv:1707.03815 (2017)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467364"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441752"},{"key":"e_1_3_2_1_6_1","volume-title":"A simple baseline algorithm for graph classification. arXiv preprint arXiv:1810.09155","author":"De Lara N.","year":"2018","unstructured":"De Lara, N., and Pineau, E. A simple baseline algorithm for graph classification. arXiv preprint arXiv:1810.09155 (2018)."},{"key":"e_1_3_2_1_7_1","volume-title":"Protein interface prediction using graph convolutional networks. Advances in neural information processing systems 30","author":"Fout A.","year":"2017","unstructured":"Fout, A., Byrd, J., Shariat, B., and Ben-Hur, A. Protein interface prediction using graph convolutional networks. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301922"},{"key":"e_1_3_2_1_9_1","volume-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31","author":"Han B.","year":"2018","unstructured":"Han, B., Yao, Q., Yu, X., Niu, G., Xu, M., Hu, W., Tsang, I., and Sugiyama, M. Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00524"},{"key":"e_1_3_2_1_11_1","first-page":"8230","volume-title":"International Conference on Machine Learning","author":"Han X.","year":"2022","unstructured":"Han, X., Jiang, Z., Liu, N., and Hu, X. G-mixup: Graph data augmentation for graph classification. In International Conference on Machine Learning (2022), PMLR, pp. 8230--8248."},{"key":"e_1_3_2_1_12_1","volume-title":"Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531","author":"Hinton G.","year":"2015","unstructured":"Hinton, G., Vinyals, O., and Dean, J. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)."},{"key":"e_1_3_2_1_13_1","first-page":"2304","volume-title":"International conference on machine learning","author":"Jiang L.","year":"2018","unstructured":"Jiang, L., Zhou, Z., Leung, T., Li, L.-J., and Fei-Fei, L. Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels. In International conference on machine learning (2018), PMLR, pp. 2304--2313."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5866"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00019"},{"key":"e_1_3_2_1_16_1","volume-title":"Semi-supervised classification with graph convo- lutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf T. N.","year":"2016","unstructured":"Kipf, T. N., and Welling, M. Semi-supervised classification with graph convo- lutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-75762-5_42"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM41043.2020.9155370"},{"key":"e_1_3_2_1_20_1","volume-title":"Simple contrastive graph clustering. arXiv preprint arXiv:2205.07865","author":"Liu Y.","year":"2022","unstructured":"Liu, Y., Yang, X., Zhou, S., and Liu, X. Simple contrastive graph clustering. arXiv preprint arXiv:2205.07865 (2022)."},{"key":"e_1_3_2_1_21_1","volume-title":"Co-matching: Combating noisy labels by augmentation anchoring. arXiv preprint arXiv:2103.12814","author":"Lu Y.","year":"2021","unstructured":"Lu, Y., Bo, Y., and He, W. Co-matching: Combating noisy labels by augmentation anchoring. arXiv preprint arXiv:2103.12814 (2021)."},{"key":"e_1_3_2_1_22_1","volume-title":"Selc: Self-ensemble label correction improves learning with noisy labels. arXiv preprint arXiv:2205.01156","author":"Lu Y.","year":"2022","unstructured":"Lu, Y., and He, W. Selc: Self-ensemble label correction improves learning with noisy labels. arXiv preprint arXiv:2205.01156 (2022)."},{"key":"e_1_3_2_1_23_1","volume-title":"Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868","author":"Shchur O.","year":"2018","unstructured":"Shchur, O., Mumme, M., Bojchevski, A., and G\u00fcnnemann, S. Pitfalls of graph neural network evaluation. arXiv preprint arXiv:1811.05868 (2018)."},{"key":"e_1_3_2_1_24_1","first-page":"74","volume-title":"ICML","volume":"2005","author":"Sindhwani V.","year":"2005","unstructured":"Sindhwani, V., Niyogi, P., and Belkin, M. A co-regularization approach to semi-supervised learning with multiple views. In ICML (2005), vol. 2005, Citeseer, pp. 74--79."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00524"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475622"},{"key":"e_1_3_2_1_27_1","volume-title":"Graph clustering with graph neural networks. arXiv preprint arXiv:2006.16904","author":"Tsitsulin A.","year":"2020","unstructured":"Tsitsulin, A., Palowitch, J., Perozzi, B., and M\u00fcller, E. Graph clustering with graph neural networks. arXiv preprint arXiv:2006.16904 (2020)."},{"key":"e_1_3_2_1_28_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veli\u010dkovi\u0107 P.","year":"2017","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., and Bengio, Y. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00041"},{"key":"e_1_3_2_1_30_1","volume-title":"Seminll: A framework of noisy-label learning by semi-supervised learning. arXiv preprint arXiv:2012.00925","author":"Wang Z.","year":"2020","unstructured":"Wang, Z., Jiang, J., Han, B., Feng, L., An, B., Niu, G., and Long, G. Seminll: A framework of noisy-label learning by semi-supervised learning. arXiv preprint arXiv:2012.00925 (2020)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01374"},{"key":"e_1_3_2_1_32_1","first-page":"40","volume-title":"International conference on machine learning","author":"Yang Z.","year":"2016","unstructured":"Yang, Z., Cohen, W., and Salakhudinov, R. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning (2016), PMLR, pp. 40--48."},{"key":"e_1_3_2_1_33_1","volume-title":"Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875","author":"Yu B.","year":"2017","unstructured":"Yu, B., Yin, H., and Zhu, Z. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i16.17680"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3446776"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00381"},{"key":"e_1_3_2_1_37_1","unstructured":"Zhang M. and Chen Y. Link prediction based on graph neural networks. Advances in neural information processing systems 31 (2018)"}],"event":{"name":"MM '23: The 31st ACM International Conference on Multimedia","location":"Ottawa ON Canada","acronym":"MM '23","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 31st ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3613117","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3581783.3613117","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:02:50Z","timestamp":1755820970000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3581783.3613117"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,26]]},"references-count":37,"alternative-id":["10.1145\/3581783.3613117","10.1145\/3581783"],"URL":"https:\/\/doi.org\/10.1145\/3581783.3613117","relation":{},"subject":[],"published":{"date-parts":[[2023,10,26]]},"assertion":[{"value":"2023-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}