{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T05:22:48Z","timestamp":1755926568106,"version":"3.37.3"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T00:00:00Z","timestamp":1620691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["PD\/BD\/114189\/2016","UIDB\/50014\/2020"],"award-info":[{"award-number":["PD\/BD\/114189\/2016","UIDB\/50014\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"publisher","award":["KK.01.1.1.01.0009","RC.2.2.08-0022"],"award-info":[{"award-number":["KK.01.1.1.01.0009","RC.2.2.08-0022"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2023,2]]},"DOI":"10.1007\/s10994-021-05979-8","type":"journal-article","created":{"date-parts":[[2021,5,11]],"date-time":"2021-05-11T17:04:21Z","timestamp":1620752661000},"page":"459-481","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks"],"prefix":"10.1007","volume":"112","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-7155","authenticated-orcid":false,"given":"Sofia","family":"Fernandes","sequence":"first","affiliation":[]},{"given":"Hadi","family":"Fanaee-T","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o","family":"Gama","sequence":"additional","affiliation":[]},{"given":"Leo","family":"Ti\u0161ljari\u0107","sequence":"additional","affiliation":[]},{"given":"Tomislav","family":"\u0160muc","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,11]]},"reference":[{"key":"5979_CR1","unstructured":"Akoglu, L., & Faloutsos, C., (2010). Event detection in time series of mobile communication graphs. In: Army Science Conference, pp. 77\u201379"},{"issue":"3","key":"5979_CR2","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2015","unstructured":"Akoglu, L., Tong, H., & Koutra, D. (2015). Graph based anomaly detection and description: A survey. Data mining and knowledge discovery, 29(3), 626\u2013688.","journal-title":"Data mining and knowledge discovery"},{"issue":"1","key":"5979_CR3","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1137\/060676489","volume":"30","author":"BW Bader","year":"2007","unstructured":"Bader, B. W., & Kolda, T. G. (2007). Efficient MATLAB computations with sparse and factored tensors. SIAM Journal on Scientific Computing, 30(1), 205\u2013231. https:\/\/doi.org\/10.1137\/060676489.","journal-title":"SIAM Journal on Scientific Computing"},{"key":"5979_CR4","unstructured":"Bader, B.W., & Kolda, T.G., et\u00a0al. (2015). Matlab tensor toolbox version 2.6. Available online . http:\/\/www.sandia.gov\/~tgkolda\/TensorToolbox\/"},{"key":"5979_CR5","doi-asserted-by":"crossref","unstructured":"Berlingerio, M., Koutra, D., Eliassi-Rad, T., & Faloutsos, C., (2013). Network similarity via multiple social theories. In: Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE\/ACM International Conference on, pp. 1439\u20131440. IEEE","DOI":"10.1145\/2492517.2492582"},{"key":"5979_CR6","doi-asserted-by":"crossref","unstructured":"Berry, M. W., & Browne, M. (2005). Understanding search engines: Mahematical modeling and text retrieval. Siam","DOI":"10.1137\/1.9780898718164"},{"issue":"1","key":"5979_CR7","doi-asserted-by":"publisher","first-page":"25","DOI":"10.7307\/ptt.v32i1.3296","volume":"32","author":"T Cari\u0107","year":"2020","unstructured":"Cari\u0107, T., & Fosin, J. (2020). Using congestion zones for solving the time dependent vehicle routing problem. Promet - Traffic&Transportation, 32(1), 25\u201338. https:\/\/doi.org\/10.7307\/ptt.v32i1.3296.","journal-title":"Promet - Traffic&Transportation"},{"issue":"4","key":"5979_CR8","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1137\/110859063","volume":"33","author":"EC Chi","year":"2012","unstructured":"Chi, E. C., & Kolda, T. G. (2012). On tensors, sparsity, and nonnegative factorizations. SIAM Journal on Matrix Analysis and Applications, 33(4), 1272\u20131299.","journal-title":"SIAM Journal on Matrix Analysis and Applications"},{"issue":"12","key":"5979_CR9","doi-asserted-by":"publisher","first-page":"1804","DOI":"10.14778\/2824032.2824077","volume":"8","author":"A Ching","year":"2015","unstructured":"Ching, A., Edunov, S., Kabiljo, M., Logothetis, D., & Muthukrishnan, S. (2015). One trillion edges: Graph processing at facebook-scale. Proceedings of the VLDB Endowment, 8(12), 1804\u20131815.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"5979_CR10","unstructured":"Costa, P., (2018). Online network analysis of stock markets. Master\u2019s thesis, University of Porto"},{"key":"5979_CR11","doi-asserted-by":"publisher","unstructured":"Dawson, R. (2011). How significant is a boxplot outlier? Journal of Statistics Education, 19(2). https:\/\/doi.org\/10.1080\/10691898.2011.11889610","DOI":"10.1080\/10691898.2011.11889610"},{"key":"5979_CR12","doi-asserted-by":"crossref","unstructured":"Desmier, E., Plantevit, M., Robardet, C., & Boulicaut, J.F., (2012). Cohesive co-evolution patterns in dynamic attributed graphs. In: International Conference on Discovery Science, pp. 110\u2013124. Springer","DOI":"10.1007\/978-3-642-33492-4_11"},{"issue":"2","key":"5979_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1921632.1921636","volume":"5","author":"DM Dunlavy","year":"2011","unstructured":"Dunlavy, D. M., Kolda, T. G., & Acar, E. (2011). Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(2), 1\u201327.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"4","key":"5979_CR14","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1007\/s00779-005-0046-3","volume":"10","author":"N Eagle","year":"2006","unstructured":"Eagle, N., & Pentland, A. S. (2006). Reality mining: Sensing complex social systems. Personal and ubiquitous computing, 10(4), 255\u2013268.","journal-title":"Personal and ubiquitous computing"},{"key":"5979_CR15","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.neucom.2016.04.006","volume":"203","author":"H Fanaee-T","year":"2016","unstructured":"Fanaee-T, H., & Gama, J. (2016). Event detection from traffic tensors: A hybrid model. Neurocomputing, 203, 22\u201333.","journal-title":"Neurocomputing"},{"key":"5979_CR16","doi-asserted-by":"crossref","unstructured":"Fernandes, S., Fanaee-T, H., & Gama, J. (2019). Evolving social networks analysis via tensor decompositions: From global event detection towards local pattern discovery and specification. In: International Conference on Discovery Science, pp. 385\u2013395. Springer","DOI":"10.1007\/978-3-030-33778-0_29"},{"issue":"5","key":"5979_CR17","doi-asserted-by":"publisher","first-page":"1002","DOI":"10.1080\/10556788.2015.1009977","volume":"30","author":"S Hansen","year":"2015","unstructured":"Hansen, S., Plantenga, T., & Kolda, T. G. (2015). Newton-based optimization for kullback-leibler nonnegative tensor factorizations. Optimization Methods and Software, 30(5), 1002\u20131029.","journal-title":"Optimization Methods and Software"},{"key":"5979_CR18","unstructured":"Hosseinimotlagh, S., & Papalexakis, E.E. (2018). Unsupervised content-based identification of fake news articles with tensor decomposition ensembles. In: Proceedings of the Workshop on Misinformation and Misbehavior Mining on the Web (MIS2)"},{"key":"5979_CR19","doi-asserted-by":"crossref","unstructured":"Isella, L., Stehl\u00e9, J., Barrat, A., Cattuto, C., Pinton, J.F., & Van\u00a0den Broeck, W., (2011). What\u2019s in a crowd? analysis of face-to-face behavioral networks. Journal of theoretical biology 271(1), 166\u2013180 http:\/\/www.sociopatterns.org\/datasets\/infectious-sociopatterns-dynamic-contact-networks\/","DOI":"10.1016\/j.jtbi.2010.11.033"},{"key":"5979_CR20","doi-asserted-by":"crossref","unstructured":"Jeon, B., Jeon, I., Sael, L., & Kang, U., (2016). Scout: Scalable coupled matrix-tensor factorization-algorithm and discoveries. In: 2016 IEEE 32nd International Conference on Data Engineering (ICDE), pp. 811\u2013822. IEEE","DOI":"10.1109\/ICDE.2016.7498292"},{"key":"5979_CR21","doi-asserted-by":"publisher","first-page":"436","DOI":"10.21914\/anziamj.v48i0.47","volume":"48","author":"KM Kapsabelis","year":"2007","unstructured":"Kapsabelis, K. M., Dickinson, P. J., & Dogancay, K. (2007). Investigation of graph edit distance cost functions for detection of network anomalies. ANZIAM Journal, 48, 436\u2013449.","journal-title":"ANZIAM Journal"},{"issue":"3","key":"5979_CR22","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1137\/07070111X","volume":"51","author":"TG Kolda","year":"2009","unstructured":"Kolda, T. G., & Bader, B. W. (2009). Tensor decompositions and applications. SIAM review, 51(3), 455\u2013500.","journal-title":"SIAM review"},{"key":"5979_CR23","doi-asserted-by":"crossref","unstructured":"Kolda, T.G., & Sun, J., (2008). Scalable tensor decompositions for multi-aspect data mining. In: 2008 Eighth IEEE International Conference on Data Mining, pp. 363\u2013372","DOI":"10.1109\/ICDM.2008.89"},{"key":"5979_CR24","doi-asserted-by":"crossref","unstructured":"Koutra, D., Papalexakis, E.E., & Faloutsos, C. (2012). Tensorsplat: Spotting latent anomalies in time. In: Proceedings of the 2012 16th Panhellenic Conference on Informatics, PCI \u201912, pp. 144\u2013149. IEEE Computer Society","DOI":"10.1109\/PCi.2012.60"},{"key":"5979_CR25","first-page":"707","volume":"10","author":"VI Levenshtein","year":"1966","unstructured":"Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady, 10, 707\u2013710.","journal-title":"Soviet physics doklady"},{"key":"5979_CR26","doi-asserted-by":"crossref","unstructured":"Michalski, R., Palus, S., & Kazienko, P. (2011). Matching organizational structure and social network extracted from email communication. In: Lecture Notes in Business Information Processing, vol.\u00a087, pp. 197\u2013206. Springer Berlin Heidelberg","DOI":"10.1007\/978-3-642-21863-7_17"},{"key":"5979_CR27","unstructured":"Papadimitriou, S., Sun, J., & Faloutsos, C. (2005). Streaming pattern discovery in multiple time-series"},{"key":"5979_CR28","doi-asserted-by":"crossref","unstructured":"Papalexakis, E., Pelechrinis, K., & Faloutsos, C. (2014). Spotting misbehaviors in location-based social networks using tensors. In: Proceedings of the companion publication of the 23rd international conference on World wide web companion, pp. 551\u2013552. International World Wide Web Conferences Steering Committee","DOI":"10.1145\/2567948.2576950"},{"key":"5979_CR29","doi-asserted-by":"crossref","unstructured":"Papalexakis, E.E., (2016). Automatic unsupervised tensor mining with quality assessment. In: Proceedings of the 2016 SIAM International Conference on Data Mining, pp. 711\u2013719. SIAM","DOI":"10.1137\/1.9781611974348.80"},{"key":"5979_CR30","doi-asserted-by":"crossref","unstructured":"Papalexakis, E.E., Faloutsos, C., & Sidiropoulos, N.D., (2012). Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I, chap. ParCube: Sparse Parallelizable Tensor Decompositions, pp. 521\u2013536. Springer Berlin Heidelberg, Berlin, Heidelberg","DOI":"10.1007\/978-3-642-33460-3_39"},{"issue":"3","key":"5979_CR31","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/s10588-005-5378-z","volume":"11","author":"CE Priebe","year":"2005","unstructured":"Priebe, C. E., Conroy, J. M., Marchette, D. J., & Park, Y. (2005). Scan statistics on enron graphs. Computational & Mathematical Organization Theory, 11(3), 229\u2013247.","journal-title":"Computational & Mathematical Organization Theory"},{"issue":"3","key":"5979_CR32","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/wics.1347","volume":"7","author":"S Ranshous","year":"2015","unstructured":"Ranshous, S., Shen, S., Koutra, D., Harenberg, S., Faloutsos, C., & Samatova, N. F. (2015). Anomaly detection in dynamic networks: a survey. Wiley Interdisciplinary Reviews: Computational Statistics, 7(3), 223\u2013247.","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"key":"5979_CR33","unstructured":"Rayana, S., & Akoglu, L., (2014). An ensemble approach for event detection and characterization in dynamic graphs. In: ACM SIGKDD ODD Workshop"},{"issue":"4","key":"5979_CR34","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1145\/2890508","volume":"10","author":"S Rayana","year":"2016","unstructured":"Rayana, S., & Akoglu, L. (2016). Less is more: Building selective anomaly ensembles. ACM Transactions on Knowledge Discovery from Data (TKDD), 10(4), 42.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"5979_CR35","doi-asserted-by":"crossref","unstructured":"Shin, K., Hooi, B., & Faloutsos, C. (2016). M-zoom: Fast dense-block detection in tensors with quality guarantees. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 264\u2013280. Springer","DOI":"10.1007\/978-3-319-46128-1_17"},{"key":"5979_CR36","doi-asserted-by":"crossref","unstructured":"Shin, K., Hooi, B., Kim, J., & Faloutsos, C., (2017). D-cube: Dense-block detection in terabyte-scale tensors. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, pp. 681\u2013689. ACM","DOI":"10.1145\/3018661.3018676"},{"issue":"01n02","key":"5979_CR37","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1142\/S0219265902000562","volume":"3","author":"P Shoubridge","year":"2002","unstructured":"Shoubridge, P., Kraetzl, M., WALLIS, W., & Bunke, H. (2002). Detection of abnormal change in a time series of graphs. Journal of Interconnection Networks, 3(01n02), 85\u2013101.","journal-title":"Journal of Interconnection Networks"},{"key":"5979_CR38","doi-asserted-by":"crossref","unstructured":"da\u00a0Silva\u00a0Fernandes, S., Tork, H.F., & da\u00a0Gama, J.M.P., (2017). The initialization and parameter setting problem in tensor decomposition-based link prediction. In: 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 99\u2013108. IEEE","DOI":"10.1109\/DSAA.2017.83"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-05979-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-021-05979-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-021-05979-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,2]],"date-time":"2023-02-02T19:16:54Z","timestamp":1675365414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-021-05979-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,11]]},"references-count":38,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,2]]}},"alternative-id":["5979"],"URL":"https:\/\/doi.org\/10.1007\/s10994-021-05979-8","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"type":"print","value":"0885-6125"},{"type":"electronic","value":"1573-0565"}],"subject":[],"published":{"date-parts":[[2021,5,11]]},"assertion":[{"value":"27 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 February 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}