{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T17:18:26Z","timestamp":1764782306656,"version":"3.37.3"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T00:00:00Z","timestamp":1624492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["World Wide Web"],"published-print":{"date-parts":[[2022,3]]},"DOI":"10.1007\/s11280-021-00906-2","type":"journal-article","created":{"date-parts":[[2021,6,24]],"date-time":"2021-06-24T11:03:51Z","timestamp":1624532631000},"page":"703-721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Cyclic label propagation for graph semi-supervised learning"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-0351","authenticated-orcid":false,"given":"Zhao","family":"Li","sequence":"first","affiliation":[]},{"given":"Yixin","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Jianliang","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Jiajun","family":"Bu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,24]]},"reference":[{"issue":"Nov","key":"906_CR1","first-page":"2399","volume":"7","author":"M Belkin","year":"2006","unstructured":"Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. JMLR 7 (Nov), 2399\u20132434 (2006)","journal-title":"JMLR"},{"key":"906_CR2","doi-asserted-by":"crossref","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: COMPSTAT, pp 177\u2013186. Springer (2010)","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"906_CR3","doi-asserted-by":"crossref","unstructured":"Bui, T.D., Ravi, S., Ramavajjala, V.: Neural graph learning: Training neural networks using graphs. In: WSDM, pp 64\u201371. ACM (2018)","DOI":"10.1145\/3159652.3159731"},{"key":"906_CR4","doi-asserted-by":"crossref","unstructured":"Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., Sun, X.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view. In: AAAI, pp 3438\u20133445 (2020)","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"906_CR5","unstructured":"Duran, A.G., Niepert, M.: Learning graph representations with embedding propagation. In: NIPS, pp 5119\u20135130 (2017)"},{"key":"906_CR6","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P. F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: ICML (2017)"},{"key":"906_CR7","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: Scalable feature learning for networks. In: SIGKDD, pp 855\u2013864. ACM (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"906_CR8","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS, pp 1024\u20131034 (2017)"},{"key":"906_CR9","doi-asserted-by":"crossref","unstructured":"Huang, X., Li, J., Hu, X.: Accelerated attributed network embedding. In: SDM, pp 633\u2013641. SIAM (2017)","DOI":"10.1137\/1.9781611974973.71"},{"key":"906_CR10","doi-asserted-by":"publisher","unstructured":"Iscen, A., Tolias, G., Avrithis, Y., Chum, O.: Label propagation for deep semi-supervised learning. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, pp. 5070\u20135079. Computer Vision Foundation \/ IEEE. https:\/\/doi.org\/10.1109\/CVPR.2019.00521 (2019)","DOI":"10.1109\/CVPR.2019.00521"},{"key":"906_CR11","doi-asserted-by":"crossref","unstructured":"Jacob, Y., Denoyer, L., Gallinari, P.: Learning latent representations of nodes for classifying in heterogeneous social networks. In: WSDM, pp 373\u2013382. ACM (2014)","DOI":"10.1145\/2556195.2556225"},{"key":"906_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"906_CR13","unstructured":"Kudo, T., Maeda, E., Matsumoto, Y.: An application of boosting to graph classification. In: NIPS, pp 729\u2013736 (2005)"},{"key":"906_CR14","unstructured":"Kumar, M.P., Packer, B., Koller, D.: Self-paced learning for latent variable models. In: NIPS, pp 1189\u20131197 (2010)"},{"issue":"7553","key":"906_CR15","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"906_CR16","doi-asserted-by":"crossref","unstructured":"Leskovec, J., Lang, K.J., Mahoney, M.: Empirical comparison of algorithms for network community detection. In: WWW, pp 631\u2013640. ACM (2010)","DOI":"10.1145\/1772690.1772755"},{"key":"906_CR17","unstructured":"Li, H., Lin, Z.: Accelerated proximal gradient methods for nonconvex programming. In: NIPS, pp 379\u2013387. MIT Press (2015)"},{"key":"906_CR18","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"906_CR19","doi-asserted-by":"crossref","unstructured":"Liang, J., Jacobs, P., Sun, J., Parthasarathy, S.: Semi-supervised embedding in attributed networks with outliers. In: Proceedings of the 2018 SIAM International Conference on Data Mining, pp 153\u2013161. SIAM (2018)","DOI":"10.1137\/1.9781611975321.18"},{"key":"906_CR20","unstructured":"Liao, L., He, X., Zhang, H., Chua, T.S.: Attributed social network embedding. In: arXiv:1705.04969 (2017)"},{"issue":"7","key":"906_CR21","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1002\/asi.20591","volume":"58","author":"D Liben-Nowell","year":"2007","unstructured":"Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of the American Society for Information Science and Technology 58(7), 1019\u20131031 (2007)","journal-title":"Journal of the American Society for Information Science and Technology"},{"key":"906_CR22","unstructured":"Liu, L., Zhou, T., Long, G., Jiang, J., Dong, X., Zhang, C.: Isometric propagation network for generalized zero-shot learning. In: International Conference on Learning Representations (ICLR) (2021)"},{"key":"906_CR23","doi-asserted-by":"crossref","unstructured":"Liu, L., Zhou, T., Long, G., Jiang, J., Zhang, C.: Attribute propagation network for graph zero-shot learning. In: AAAI Conference on Artificial Intelligence (AAAI) (2020)","DOI":"10.1609\/aaai.v34i04.5923"},{"key":"906_CR24","unstructured":"Lu, Q., Getoor, L.: Link-based classification. In: ICML, pp 496\u2013503 (2003)"},{"key":"906_CR25","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)"},{"key":"906_CR26","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp 3111\u20133119 (2013)"},{"key":"906_CR27","unstructured":"Pan, S., Wu, J., Zhu, X., Zhang, C., Wang, Y.: Tri-party deep network representation. In: IJCAI, pp 1895\u20131901 (2016)"},{"issue":"3","key":"906_CR28","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1561\/2400000003","volume":"1","author":"N Parikh","year":"2014","unstructured":"Parikh, N., Boyd, S.: Proximal algorithms. Foundations and Trends in Optimization 1(3), 127\u2013239 (2014)","journal-title":"Foundations and Trends in Optimization"},{"key":"906_CR29","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., Skiena, S.: Deepwalk: Online learning of social representations. In: SIGKDD, pp 701\u2013710. ACM (2014)","DOI":"10.1145\/2623330.2623732"},{"key":"906_CR30","unstructured":"Rong, Y., Huang, W., Xu, T., Huang, J.: Dropedge: Towards deep graph convolutional networks on node classification. In: International Conference on Learning Representations (2019)"},{"key":"906_CR31","doi-asserted-by":"crossref","unstructured":"Shi, W., Rajkumar, R.: Point-gnn: Graph neural network for 3d object detection in a point cloud. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 1711\u20131719 (2020)","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"906_CR32","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Mei, Q.: Pte: Predictive text embedding through large-scale heterogeneous text networks. In: SIGKDD, pp 1165\u20131174. ACM (2015)","DOI":"10.1145\/2783258.2783307"},{"key":"906_CR33","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: Large-scale information network embedding. In: WWW, pp 1067\u20131077. ACM (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"906_CR34","doi-asserted-by":"crossref","unstructured":"Tang, L., Liu, H.: Relational learning via latent social dimensions. In: SIGKDD, pp 817\u2013826. ACM (2009)","DOI":"10.1145\/1557019.1557109"},{"key":"906_CR35","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)"},{"key":"906_CR36","unstructured":"Veli\u010dkovi\u0107, P., Fedus, W., Hamilton, W.L., Li\u00f2, P., Bengio, Y., Hjelm, R.D.: Deep graph infomax. In: International Conference on Learning Representations (2019)"},{"key":"906_CR37","doi-asserted-by":"crossref","unstructured":"Wang, B., Tu, Z., Tsotsos, J.K.: Dynamic label propagation for semi-supervised multi-class multi-label classification. In: ICCV, pp. 425\u2013432 (2013)","DOI":"10.1109\/ICCV.2013.60"},{"key":"906_CR38","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, C.: Label propagation through linear neighborhoods. In: ICML, pp 985\u2013992. ACM (2006)","DOI":"10.1145\/1143844.1143968"},{"key":"906_CR39","unstructured":"Wang, H., Leskovec, J.: Unifying graph convolutional neural networks and label propagation. arXiv:2002.06755 (2020)"},{"key":"906_CR40","unstructured":"Wang, W., Carreira-Perpin\u00e1n, M.A.: Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application. In: arXiv:1309.1541 (2013)"},{"key":"906_CR41","unstructured":"Wu, F., Souza, Jr, A.H., Zhang, T., Fifty, C., Yu, T., Weinberger, K.Q.: Simplifying graph convolutional networks. In: ICML (2019)"},{"key":"906_CR42","doi-asserted-by":"crossref","unstructured":"Wu, L., Wang, D., Feng, S., Zhang, Y., Yu, G.: Mdal: Multi-task dual attention lstm model for semi-supervised network embedding. In: International Conference on Database Systems for Advanced Applications, pp 468\u2013483. Springer (2019)","DOI":"10.1007\/978-3-030-18576-3_28"},{"key":"906_CR43","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems (2020)","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"906_CR44","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Long, G., Jiang, J., Chang, X., Zhang, C.: Connecting the dots: Multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 753\u2013763 (2020)","DOI":"10.1145\/3394486.3403118"},{"key":"906_CR45","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks?. In: International Conference on Learning Representations (2019)"},{"key":"906_CR46","unstructured":"Yang, Z., Cohen, W.W., Salakhutdinov, R.: Revisiting semi-supervised learning with graph embeddings. In: ICML, pp. 40\u201348. JMLR.org (2016)"},{"key":"906_CR47","unstructured":"Ying, Z., Bourgeois, D., You, J., Zitnik, M., Leskovec, J.: GNNExplainer: Generating explanations for graph neural networks. In: Advances in neural information processing systems, pp. 9244\u20139255 (2019)"},{"key":"906_CR48","unstructured":"Zhou, D., Bousquet, O., Lal, T. N., Weston, J., Sch\u00f6lkopf, B.: Learning with local and global consistency. In: NIPS, pp. 321\u2013328 (2004)"},{"key":"906_CR49","unstructured":"Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using Gaussian fields and harmonic functions. In: ICML, pp. 912\u2013919 (2003)"}],"container-title":["World Wide Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-021-00906-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11280-021-00906-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11280-021-00906-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T11:04:23Z","timestamp":1672571063000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11280-021-00906-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,24]]},"references-count":49,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,3]]}},"alternative-id":["906"],"URL":"https:\/\/doi.org\/10.1007\/s11280-021-00906-2","relation":{},"ISSN":["1386-145X","1573-1413"],"issn-type":[{"type":"print","value":"1386-145X"},{"type":"electronic","value":"1573-1413"}],"subject":[],"published":{"date-parts":[[2021,6,24]]},"assertion":[{"value":"19 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 June 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}