{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T22:50:01Z","timestamp":1753051801446,"version":"3.40.4"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030461492"},{"type":"electronic","value":"9783030461508"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46150-8_10","type":"book-chapter","created":{"date-parts":[[2020,5,1]],"date-time":"2020-05-01T01:02:40Z","timestamp":1588294960000},"page":"156-172","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["CatchCore: Catching Hierarchical Dense Subtensor"],"prefix":"10.1007","author":[{"given":"Wenjie","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueqi","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. In: Data Mining and Knowledge Discovery (2015)","key":"10_CR1","DOI":"10.1007\/s10618-014-0365-y"},{"unstructured":"Andersen, R., Chellapilla, K.: Finding dense subgraphs with size bounds. WAW","key":"10_CR2"},{"doi-asserted-by":"crossref","unstructured":"Balalau, O.D., Bonchi, F., Chan, T.H.H., Gullo, F., Sozio, M.: Finding subgraphs with maximum total density and limited overlap. In: WSDM 2015 (2015)","key":"10_CR3","DOI":"10.1145\/2684822.2685298"},{"issue":"7","key":"10_CR4","doi-asserted-by":"publisher","first-page":"1216","DOI":"10.1109\/TKDE.2010.271","volume":"24","author":"J Chen","year":"2010","unstructured":"Chen, J., Saad, Y.: Dense subgraph extraction with application to community detection. IEEE Trans. Knowl. Eng. 24(7), 1216\u20131230 (2010)","journal-title":"IEEE Trans. Knowl. Eng."},{"issue":"2","key":"10_CR5","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1137\/0806023","volume":"6","author":"TF Coleman","year":"1996","unstructured":"Coleman, T.F., Li, Y.: An interior trust region approach for nonlinear minimization subject to bounds. SIAM J. Optim. 6(2), 418\u2013445 (1996)","journal-title":"SIAM J. Optim."},{"issue":"4","key":"10_CR6","doi-asserted-by":"publisher","first-page":"112","DOI":"10.3390\/a10040112","volume":"10","author":"D Edler","year":"2017","unstructured":"Edler, D., Bohlin, L., et al.: Mapping higher-order network flows in memory and multilayer networks with infomap. Algorithms 10(4), 112 (2017)","journal-title":"Algorithms"},{"unstructured":"Gibson, D., Kumar, R., Tomkins, A.: Discovering large dense subgraphs in massive graphs. In: VLDB 2005. VLDB Endowment (2005)","key":"10_CR7"},{"doi-asserted-by":"crossref","unstructured":"Gorovits, A., Gujral, E., Papalexakis, E.E., Bogdanov, P.: LARC: learning activity-regularized overlapping communities across time. In: SIGKDD 2018. ACM (2018)","key":"10_CR8","DOI":"10.1145\/3219819.3220118"},{"issue":"3","key":"10_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/S0167-6377(99)00074-7","volume":"26","author":"L Grippo","year":"2000","unstructured":"Grippo, L., Sciandrone, M.: On the convergence of the block nonlinear Gauss-Seidel method under convex constraints. Oper. Res. Lett. 26(3), 127\u2013136 (2000)","journal-title":"Oper. Res. Lett."},{"doi-asserted-by":"crossref","unstructured":"Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: FRAUDAR: bounding graph fraud in the face of camouflage. In: SIGKDD 2016, pp. 895\u2013904 (2016)","key":"10_CR10","DOI":"10.1145\/2939672.2939747"},{"doi-asserted-by":"crossref","unstructured":"Jiang, M., Beutel, A., Cui, P., Hooi, B., Yang, S., Faloutsos, C.: A general suspiciousness metric for dense blocks in multimodal data. In: ICDM 2015 (2015)","key":"10_CR11","DOI":"10.1109\/ICDM.2015.61"},{"doi-asserted-by":"crossref","unstructured":"Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. In: SIAM (2009)","key":"10_CR12","DOI":"10.1137\/07070111X"},{"doi-asserted-by":"crossref","unstructured":"Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: Link Mining: Models, Algorithms, and Applications (2010)","key":"10_CR13","DOI":"10.1007\/978-1-4419-6515-8_13"},{"doi-asserted-by":"crossref","unstructured":"Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Statistical properties of community structure in large social and information networks. In: WWW (2008)","key":"10_CR14","DOI":"10.1145\/1367497.1367591"},{"issue":"10","key":"10_CR15","doi-asserted-by":"publisher","first-page":"2756","DOI":"10.1162\/neco.2007.19.10.2756","volume":"19","author":"CJ Lin","year":"2007","unstructured":"Lin, C.J.: Projected gradient methods for nonnegative matrix factorization. Neural Comput. 19(10), 2756\u20132779 (2007)","journal-title":"Neural Comput."},{"issue":"4","key":"10_CR16","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1137\/S1052623498345075","volume":"9","author":"CJ Lin","year":"1999","unstructured":"Lin, C.J., Mor\u00e9, J.J.: Newton\u2019s method for large bound-constrained optimization problems. SIAM J. Optim. 9(4), 1100\u20131127 (1999)","journal-title":"SIAM J. Optim."},{"key":"10_CR17","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-540-87481-2_12","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Papadimitriou","year":"2008","unstructured":"Papadimitriou, S., Sun, J., Faloutsos, C., Yu, P.S.: Hierarchical, parameter-free community discovery. In: Daelemans, W., Goethals, B., Morik, K. (eds.) ECML PKDD 2008. LNCS (LNAI), vol. 5212, pp. 170\u2013187. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-87481-2_12"},{"issue":"2","key":"10_CR18","doi-asserted-by":"publisher","first-page":"416","DOI":"10.1214\/aos\/1176346150","volume":"11","author":"J Rissanen","year":"1983","unstructured":"Rissanen, J.: A universal prior for integers and estimation by minimum description length. Ann. Stat. 11(2), 416\u2013431 (1983)","journal-title":"Ann. Stat."},{"unstructured":"Sariy\u00fcce, A.E., Pinar, A.: Fast hierarchy construction for dense subgraphs. VLDB","key":"10_CR19"},{"key":"10_CR20","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1007\/978-3-319-46128-1_17","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"K Shin","year":"2016","unstructured":"Shin, K., Hooi, B., Faloutsos, C.: M-zoom: fast dense-block detection in tensors with quality guarantees. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 264\u2013280. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46128-1_17"},{"doi-asserted-by":"crossref","unstructured":"Shin, K., Hooi, B., Kim, J., Faloutsos, C.: D-cube: Dense-block detection in terabyte-scale tensors. In: WSDM 2017. ACM (2017)","key":"10_CR21","DOI":"10.1145\/3018661.3018676"},{"doi-asserted-by":"crossref","unstructured":"Shin, K., Hooi, B., Kim, J., Faloutsos, C.: DenseAlert: incremental dense-subtensor detection in tensor streams (2017)","key":"10_CR22","DOI":"10.1145\/3097983.3098087"},{"doi-asserted-by":"crossref","unstructured":"Siddique, B., Akhtar, N.: Temporal hierarchical event detection of timestamped data. In: ICCCA 2017 (2017)","key":"10_CR23","DOI":"10.1109\/CCAA.2017.8229902"},{"doi-asserted-by":"crossref","unstructured":"Tsourakakis, C., Bonchi, F., Gionis, A., Gullo, F., Tsiarli, M.: Denser than the densest subgraph: extracting optimal quasi-cliques with quality guarantees. In: SIGKDD 2013. ACM (2013)","key":"10_CR24","DOI":"10.1145\/2487575.2487645"},{"doi-asserted-by":"crossref","unstructured":"Yang, B., Di, J., Liu, J., Liu, D.: Hierarchical community detection with applications to real-world network analysis. In: DKE (2013)","key":"10_CR25","DOI":"10.1016\/j.datak.2012.09.002"},{"doi-asserted-by":"crossref","unstructured":"Zhang, S., et al.: Hidden: hierarchical dense subgraph detection with application to financial fraud detection. In: SDM 2017. SIAM (2017)","key":"10_CR26","DOI":"10.1137\/1.9781611974973.64"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46150-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T09:32:30Z","timestamp":1746523950000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46150-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461492","9783030461508"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46150-8_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","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":"130","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":"0","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":"18% - 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":"3.04","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":"5.3","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":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","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)"}}]}}