{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T00:15:21Z","timestamp":1743034521624,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031001253"},{"type":"electronic","value":"9783031001260"}],"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.springer.com\/tdm"},{"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.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-00126-0_32","type":"book-chapter","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:07:55Z","timestamp":1650996475000},"page":"423-438","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Semi-supervised Graph Learning with\u00a0Few Labeled Nodes"],"prefix":"10.1007","author":[{"given":"Cong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Ting","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Bin","family":"Wu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"key":"32_CR1","doi-asserted-by":"crossref","unstructured":"Ahmed, A., Shervashidze, N., Narayanamurthy, S.M., Josifovski, V., Smola, A.J.: Distributed large-scale natural graph factorization. In: WWW, pp. 37\u201348 (2013)","DOI":"10.1145\/2488388.2488393"},{"key":"32_CR2","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y.: Spectral networks and locally connected networks on graphs. In: ICLR (2014)"},{"key":"32_CR3","doi-asserted-by":"crossref","unstructured":"Cao, S., Lu, W., Xu, Q.: GraRep: learning graph representations with global structural information. In: CIKM, pp. 891\u2013900 (2015)","DOI":"10.1145\/2806416.2806512"},{"key":"32_CR4","unstructured":"Defferrard, M., Bresson, X., Vandergheynst, P.: Convolutional neural networks on graphs with fast localized spectral filtering. In: NIPS, pp. 3837\u20133845 (2016)"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp. 855\u2013864 (2016)","DOI":"10.1145\/2939672.2939754"},{"issue":"11","key":"32_CR6","doi-asserted-by":"publisher","first-page":"2389","DOI":"10.1080\/00207160.2013.831082","volume":"91","author":"J Gui","year":"2014","unstructured":"Gui, J., Hu, R., Zhao, Z., Jia, W.: Semi-supervised learning with local and global consistency. Int. J. Comput. Math. 91(11), 2389\u20132402 (2014)","journal-title":"Int. J. Comput. Math."},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Hamaguchi, T., Oiwa, H., Shimbo, M., Matsumoto, Y.: Knowledge transfer for out-of-knowledge-base entities: a graph neural network approach. In: IJCAI, pp. 1802\u20131808 (2017)","DOI":"10.24963\/ijcai.2017\/250"},{"key":"32_CR8","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS, pp. 1025\u20131035 (2017)"},{"key":"32_CR9","doi-asserted-by":"crossref","unstructured":"Hong, H., Guo, H., Lin, Y., Yang, X., Li, Z., Ye, J.: An attention-based graph neural network for heterogeneous structural learning. In: AAAI 2020, pp. 4132\u20134139 (2020)","DOI":"10.1609\/aaai.v34i04.5833"},{"key":"32_CR10","unstructured":"Hu, W., et al.: Open graph benchmark: datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687 (2020)"},{"key":"32_CR11","unstructured":"Huang, Q., He, H., Singh, A., Lim, S.N., Benson, A.R.: Combining label propagation and simple models out-performs graph neural networks. arXiv preprint arXiv:2010.13993 (2020)"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Hui, B., Zhu, P., Hu, Q.: Collaborative graph convolutional networks: unsupervised learning meets semi-supervised learning. In: AAAI 2020, pp. 4215\u20134222 (2020)","DOI":"10.1609\/aaai.v34i04.5843"},{"key":"32_CR13","unstructured":"Kipf, T.N., Welling, M.: Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)"},{"key":"32_CR14","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.: Deeper insights into graph convolutional networks for semi-supervised learning. In: AAAI, pp. 3538\u20133545 (2018)","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"32_CR16","unstructured":"Liu, Y., et al.: Learning to propagate labels: transductive propagation network for few-shot learning. In: ICLR (2019)"},{"key":"32_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/1756-0381-4-10","volume":"4","author":"GA Pavlopoulos","year":"2011","unstructured":"Pavlopoulos, G.A.: Using graph theory to analyze biological networks. BioData Min. 4, 10 (2011)","journal-title":"BioData Min."},{"key":"32_CR18","unstructured":"Sanchez-Gonzalez, A., et al.: Graph networks as learnable physics engines for inference and control. In: ICML, pp. 4467\u20134476 (2018)"},{"issue":"3","key":"32_CR19","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., Eliassi-Rad, T.: Collective classification in network data. AI Mag. 29(3), 93\u2013106 (2008)","journal-title":"AI Mag."},{"key":"32_CR20","doi-asserted-by":"crossref","unstructured":"Shi, Y., Huang, Z., Wang, W., Zhong, H., Feng, S., Sun, Y.: Masked label prediction: unified message passing model for semi-supervised classification. arXiv preprint arXiv:2009.03509 (2020)","DOI":"10.24963\/ijcai.2021\/214"},{"key":"32_CR21","doi-asserted-by":"crossref","unstructured":"Song, Z., Yang, X., Xu, Z., King, I.: Graph-based semi-supervised learning: a comprehensive review. arXiv preprint arXiv:2102.13303 (2021)","DOI":"10.1109\/TNNLS.2022.3155478"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Sun, K., Lin, Z., Zhu, Z.: Multi-stage self-supervised learning for graph convolutional networks on graphs with few labeled nodes. In: AAAI, pp. 5892\u20135899 (2020)","DOI":"10.1609\/aaai.v34i04.6048"},{"key":"32_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1007\/978-3-642-04174-7_29","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"PP Talukdar","year":"2009","unstructured":"Talukdar, P.P., Crammer, K.: New regularized algorithms for transductive learning. In: Buntine, W., Grobelnik, M., Mladeni\u0107, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 442\u2013457. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-04174-7_29"},{"key":"32_CR24","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 (2015)","DOI":"10.1145\/2736277.2741093"},{"key":"32_CR25","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: ICLR (2018)"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Wan, S., Pan, S., Yang, J., Gong, C.: Contrastive and generative graph convolutional networks for graph-based semi-supervised learning. In: AAAI, vol. 35, pp. 10049\u201310057 (2021)","DOI":"10.1609\/aaai.v35i11.17206"},{"key":"32_CR27","doi-asserted-by":"crossref","unstructured":"Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: KDD, pp. 1225\u20131234 (2016)","DOI":"10.1145\/2939672.2939753"},{"key":"32_CR28","unstructured":"Wang, H., Leskovec, J.: Unifying graph convolutional neural networks and label propagation. arXiv preprint arXiv:2002.06755 (2020)"},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Xu, B., Huang, J., Hou, L., Shen, H., Gao, J., Cheng, X.: Label-consistency based graph neural networks for semi-supervised node classification. In: SIGIR, pp. 1897\u20131900 (2020)","DOI":"10.1145\/3397271.3401308"},{"key":"32_CR30","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., Jegelka, S.: Representation learning on graphs with jumping knowledge networks. In: ICML, pp. 5449\u20135458 (2018)"},{"key":"32_CR31","doi-asserted-by":"crossref","unstructured":"Xu, N., Wang, P., Chen, L., Tao, J., Zhao, J.: MR-GNN: multi-resolution and dual graph neural network for predicting structured entity interactions. In: IJCAI, pp. 3968\u20133974 (2019)","DOI":"10.24963\/ijcai.2019\/551"},{"key":"32_CR32","unstructured":"Yang, Z., Cohen, W., Salakhudinov, R.: Revisiting semi-supervised learning with graph embeddings. In: ICML, pp. 40\u201348. PMLR (2016)"},{"key":"32_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Pal, S., Coates, M., \u00dcstebay, D.: Bayesian graph convolutional neural networks for semi-supervised classification. In: AAAI 2019, pp. 5829\u20135836 (2019)","DOI":"10.1609\/aaai.v33i01.33015829"},{"key":"32_CR34","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":"32_CR35","doi-asserted-by":"crossref","unstructured":"Zhou, D., Huang, J., Sch\u00f6lkopf, B.: Learning from labeled and unlabeled data on a directed graph. In: ICML, vol. 119, pp. 1036\u20131043 (2005)","DOI":"10.1145\/1102351.1102482"},{"key":"32_CR36","unstructured":"Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation (2003)"},{"key":"32_CR37","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":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-00126-0_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T16:09:24Z","timestamp":1675440564000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-00126-0_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031001253","9783031001260"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-00126-0_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2022.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"543","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was originally planned to take place in Hyberabad, India. 24 other papers are included in the volume.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}