{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T01:59:21Z","timestamp":1774663161591,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819698936","type":"print"},{"value":"9789819698943","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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\/978-981-96-9894-3_29","type":"book-chapter","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T19:57:06Z","timestamp":1753473426000},"page":"351-363","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ccTBG: Co-clustering for Temporal Bipartite Graph"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7455-5453","authenticated-orcid":false,"given":"Shenghai","family":"Zhong","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9660-0291","authenticated-orcid":false,"given":"Shu","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0179-2364","authenticated-orcid":false,"given":"Lihong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,26]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.knosys.2016.07.002","volume":"109","author":"M Ailem","year":"2016","unstructured":"Ailem, M., Role, F., Nadif, M.: Graph modularity maximization as an effective method for co-clustering text data. Knowl.-Based Syst. 109, 160\u2013173 (2016)","journal-title":"Knowl.-Based Syst."},{"key":"29_CR2","unstructured":"Airoldi, E.M., Blei, D., Fienberg, S., Xing, E.: Mixed membership stochastic blockmodels. Advances in neural information processing systems 21 (2008)"},{"issue":"2","key":"29_CR3","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1007\/s10994-021-06002-w","volume":"112","author":"E Battaglia","year":"2023","unstructured":"Battaglia, E., Pensa, R.G.: A parameter-less algorithm for tensor co-clustering. Mach. Learn. 112(2), 385\u2013427 (2023)","journal-title":"Mach. Learn."},{"issue":"4","key":"29_CR4","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1109\/TKDE.2018.2846252","volume":"31","author":"C Bl\u00f6chl","year":"2018","unstructured":"Bl\u00f6chl, C., Amjad, R.A., Geiger, B.C.: Co-clustering via information-theoretic Markov aggregation. IEEE Trans. Knowl. Data Eng. 31(4), 720\u2013732 (2018)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.neucom.2021.09.036","volume":"468","author":"R Boutalbi","year":"2022","unstructured":"Boutalbi, R., Labiod, L., Nadif, M.: Tensorclus: a python library for tensor (co)-clustering. Neurocomputing 468, 464\u2013468 (2022)","journal-title":"Neurocomputing"},{"key":"29_CR6","doi-asserted-by":"publisher","first-page":"10390","DOI":"10.1109\/ACCESS.2020.2965544","volume":"8","author":"X Cai","year":"2020","unstructured":"Cai, X., et al.: Dbge: employee turnover prediction based on dynamic bipartite graph embedding. IEEE Access 8, 10390\u201310402 (2020)","journal-title":"IEEE Access"},{"key":"29_CR7","doi-asserted-by":"crossref","unstructured":"Chen, W., Wang, H., Long, Z., Li, T.: Fast flexible bipartite graph model for co-clustering. IEEE Trans. Knowl. Data Eng. (2022)","DOI":"10.1109\/TKDE.2022.3194275"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269\u2013274 (2001)","DOI":"10.1145\/502512.502550"},{"key":"29_CR9","doi-asserted-by":"crossref","unstructured":"Dhillon, I.S., Mallela, S., Modha, D.S.: Information-theoretic co-clustering. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 89\u201398 (2003)","DOI":"10.1145\/956750.956764"},{"issue":"2","key":"29_CR10","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1109\/TII.2019.2896287","volume":"16","author":"Y Du","year":"2019","unstructured":"Du, Y., et al.: Bayesian co-clustering truth discovery for mobile crowd sensing systems. IEEE Trans. Industr. Inf. 16(2), 1045\u20131057 (2019)","journal-title":"IEEE Trans. Industr. Inf."},{"key":"29_CR11","unstructured":"Greene, D., Cunningham, P.: Spectral co-clustering for dynamic bipartite graphs. DyNaK 2010 Dynamic Networks and Knowledge Discovery, p. 29 (2010)"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Hecking, T., Steinert, L., G\u00f6hnert, T., Hoppe, H.U.: Incremental clustering of dynamic bipartite networks. In: 2014 European Network Intelligence Conference, pp. 9\u201316. IEEE (2014)","DOI":"10.1109\/ENIC.2014.15"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Krishna, V., Antulov-Fantulin, N.: Temporal-weighted bipartite graph model for sparse expert recommendation in community question answering. In: Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization, pp. 156\u2013163 (2023)","DOI":"10.1145\/3565472.3592957"},{"key":"29_CR14","doi-asserted-by":"crossref","unstructured":"Kumar, S., Zhang, X., Leskovec, J.: Predicting dynamic embedding trajectory in temporal interaction networks. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1269\u20131278 (2019)","DOI":"10.1145\/3292500.3330895"},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Labiod, L., Nadif, M.: Co-clustering for binary and categorical data with maximum modularity. In: 2011 IEEE 11th International Conference on Data Mining, pp. 1140\u20131145. IEEE (2011)","DOI":"10.1109\/ICDM.2011.37"},{"key":"29_CR16","unstructured":"Li, T., et al.: Exploring temporal community structure via network embedding. IEEE Trans. Cybern. (2022)"},{"key":"29_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2023.107836","volume":"189","author":"J Liu","year":"2024","unstructured":"Liu, J., Ye, Z., Chen, K., Zhang, P.: Variational bayesian inference for bipartite mixed membership stochastic block model with applications to collaborative filtering. Comput. Stat. Data Anal. 189, 107836 (2024)","journal-title":"Comput. Stat. Data Anal."},{"key":"29_CR18","doi-asserted-by":"crossref","unstructured":"Manning, C.D.: Introduction to information retrieval. Syngress Publishing (2008)","DOI":"10.1017\/CBO9780511809071"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Marchello, G., Corneli, M., Bouveyron, C.: A deep dynamic latent block model for coclustering of zero-inflated data matrices. J. Comput. Graphical Stat., 1\u201316 (2024)","DOI":"10.1080\/10618600.2024.2319162"},{"key":"29_CR20","doi-asserted-by":"crossref","unstructured":"Park, N., et al.: Cgc: contrastive graph clustering for community detection and tracking. In: Proceedings of the ACM Web Conference 2022, pp. 1115\u20131126 (2022)","DOI":"10.1145\/3485447.3512160"},{"issue":"1","key":"29_CR21","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s11634-022-00492-9","volume":"17","author":"P Riverain","year":"2023","unstructured":"Riverain, P., Fossier, S., Nadif, M.: Poisson degree corrected dynamic stochastic block model. Adv. Data Anal. Classif. 17(1), 135\u2013162 (2023)","journal-title":"Adv. Data Anal. Classif."},{"key":"29_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v088.i07","volume":"88","author":"F Role","year":"2019","unstructured":"Role, F., Morbieu, S., Nadif, M.: Coclust: a python package for co-clustering. J. Stat. Softw. 88, 1\u201329 (2019)","journal-title":"J. Stat. Softw."},{"key":"29_CR23","unstructured":"Rosenberg, A., Hirschberg, J.: V-measure: A conditional entropy-based external cluster evaluation measure. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 410\u2013420 (2007)"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Shan, H., Banerjee, A.: Bayesian co-clustering. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 530\u2013539. IEEE (2008)","DOI":"10.1109\/ICDM.2008.91"},{"issue":"6","key":"29_CR25","doi-asserted-by":"publisher","first-page":"2237","DOI":"10.1016\/j.patcog.2011.12.015","volume":"45","author":"F Shang","year":"2012","unstructured":"Shang, F., Jiao, L., Wang, F.: Graph dual regularization non-negative matrix factorization for co-clustering. Pattern Recogn. 45(6), 2237\u20132250 (2012)","journal-title":"Pattern Recogn."},{"issue":"1","key":"29_CR26","first-page":"1","volume":"1","author":"L Shen","year":"2024","unstructured":"Shen, L., Amini, A., Josephs, N., Lin, L.: Bayesian community detection for networks with covariates. Bayesian Anal. 1(1), 1\u201328 (2024)","journal-title":"Bayesian Anal."},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Wang, A.Z., Ying, R., Li, P., Rao, N., Subbian, K., Leskovec, J.: Bipartite dynamic representations for abuse detection. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 3638\u20133648 (2021)","DOI":"10.1145\/3447548.3467141"},{"key":"29_CR28","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.eswa.2017.01.019","volume":"78","author":"S Wang","year":"2017","unstructured":"Wang, S., Huang, A.: Penalized nonnegative matrix tri-factorization for co-clustering. Expert Syst. Appl. 78, 64\u201373 (2017)","journal-title":"Expert Syst. Appl."},{"key":"29_CR29","unstructured":"Xie, J., Girshick, R., Farhadi, A.: Unsupervised deep embedding for clustering analysis. In: International Conference on Machine Learning, pp. 478\u2013487. PMLR (2016)"},{"key":"29_CR30","unstructured":"Yang, C., Liu, M., Wang, Z., Liu, L., Han, J.: Graph clustering with dynamic embedding. arXiv preprint arXiv:1712.08249 (2017)"},{"key":"29_CR31","doi-asserted-by":"crossref","unstructured":"Yang, R., Shi, J., Huang, K., Xiao, X.: Scalable and effective bipartite network embedding. In: Proceedings of the 2022 International Conference on Management of Data, pp. 1977\u20131991 (2022)","DOI":"10.1145\/3514221.3517838"},{"key":"29_CR32","doi-asserted-by":"crossref","unstructured":"Yao, Y., Joe-Wong, C.: Interpretable clustering on dynamic graphs with recurrent graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4608\u20134616 (2021)","DOI":"10.1609\/aaai.v35i5.16590"},{"issue":"5","key":"29_CR33","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1016\/j.ipm.2009.12.007","volume":"46","author":"J Yoo","year":"2010","unstructured":"Yoo, J., Choi, S.: Orthogonal nonnegative matrix tri-factorization for co-clustering: Multiplicative updates on stiefel manifolds. Inf. Process. Manage. 46(5), 559\u2013570 (2010)","journal-title":"Inf. Process. Manage."},{"key":"29_CR34","doi-asserted-by":"crossref","unstructured":"You, J., Hu, C., Kamigaito, H., Funakoshi, K., Okumura, M.: Robust dynamic clustering for temporal networks. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2424\u20132433 (2021)","DOI":"10.1145\/3459637.3482473"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9894-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T01:36:41Z","timestamp":1774661801000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9894-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819698936","9789819698943"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9894-3_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"26 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}