{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:59:14Z","timestamp":1743044354861,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030757618"},{"type":"electronic","value":"9783030757625"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-75762-5_32","type":"book-chapter","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T09:07:43Z","timestamp":1620464863000},"page":"395-407","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Discovering Dense Correlated Subgraphs in Dynamic Networks"],"prefix":"10.1007","author":[{"given":"Giulia","family":"Preti","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Polina","family":"Rozenshtein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aristides","family":"Gionis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yannis","family":"Velegrakis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,5,9]]},"reference":[{"issue":"12","key":"32_CR1","first-page":"2710","volume":"29","author":"E Abdelhamid","year":"2017","unstructured":"Abdelhamid, E., Canim, M., Sadoghi, M., Bhattacharjee, B., Chang, Y.C., Kalnis, P.: Incremental frequent subgraph mining on large evolving graphs. TKDE 29(12), 2710\u20132723 (2017)","journal-title":"TKDE"},{"issue":"3","key":"32_CR2","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1007\/s10618-014-0365-y","volume":"29","author":"L Akoglu","year":"2014","unstructured":"Akoglu, L., Tong, H., Koutra, D.: Graph based anomaly detection and description: a survey. Data Min. Knowl Disc. 29(3), 626\u2013688 (2014). https:\/\/doi.org\/10.1007\/s10618-014-0365-y","journal-title":"Data Min. Knowl Disc."},{"issue":"3","key":"32_CR3","first-page":"630","volume":"60","author":"AZ Broder","year":"2000","unstructured":"Broder, A.Z., Charikar, M., Frieze, A.M., Mitzenmacher, M.: Min-wise independent permutations. JCSS 60(3), 630\u2013659 (2000)","journal-title":"JCSS"},{"issue":"6","key":"32_CR4","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1109\/TMC.2007.1060","volume":"6","author":"A Chaintreau","year":"2007","unstructured":"Chaintreau, A., Hui, P., Crowcroft, J., Diot, C., Gass, R., Scott, J.: Impact of human mobility on opportunistic forwarding algorithms. Trans. Mob. Comput. 6(6), 606\u2013620 (2007)","journal-title":"Trans. Mob. Comput."},{"issue":"1","key":"32_CR5","first-page":"53","volume":"16","author":"J Chan","year":"2008","unstructured":"Chan, J., Bailey, J., Leckie, C.: Discovering correlated spatio-temporal changes in evolving graphs. KAIS 16(1), 53\u201396 (2008)","journal-title":"KAIS"},{"key":"32_CR6","doi-asserted-by":"crossref","unstructured":"Chan, J., Bailey, J., Leckie, C., Houle, M.: ciForager: incrementally discovering regions of correlated change in evolving graphs. TKDD (2012)","DOI":"10.1145\/2362383.2362385"},{"key":"32_CR7","doi-asserted-by":"crossref","unstructured":"Charikar, M.: Greedy approximation algorithms for finding dense components in a graph. In: APPROX (2000)","DOI":"10.1007\/3-540-44436-X_10"},{"issue":"1","key":"32_CR8","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.engappai.2010.09.007","volume":"24","author":"TC Fu","year":"2011","unstructured":"Fu, T.C.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164\u2013181 (2011)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"5","key":"32_CR9","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1007\/s10618-016-0464-z","volume":"30","author":"E Galbrun","year":"2016","unstructured":"Galbrun, E., Gionis, A., Tatti, N.: Top-$$k$$ overlapping densest subgraphs. Data Min. Knowl. Disc. 30(5), 1134\u20131165 (2016)","journal-title":"Data Min. Knowl. Disc."},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Guan, Z., Yan, X., Kaplan, L.M.: Measuring two-event structural correlations on graphs. PVLDB 5(11) (2012)","DOI":"10.14778\/2350229.2350256"},{"key":"32_CR11","doi-asserted-by":"publisher","unstructured":"Italia, T.: Telecommunications - tn to tn (2015). https:\/\/doi.org\/10.7910\/DVN\/KCRS61","DOI":"10.7910\/DVN\/KCRS61"},{"key":"32_CR12","doi-asserted-by":"crossref","unstructured":"Lahoti, P., Garimella, K., Gionis, A.: Joint non-negative matrix factorization for learning ideological leaning on twitter. In: WSDM (2018)","DOI":"10.1145\/3159652.3159669"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Ma, S., Hu, R., Wang, L., Lin, X., Huai, J.: Fast computation of dense temporal subgraphs. In: ICDE (2017)","DOI":"10.1109\/ICDE.2017.95"},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Preti, G., Rozenshtein, P., Gionis, A., Velegrakis, Y.: Excode: a tool for discovering and visualizing regions of correlation in dynamic networks. In: ICDMW (2019)","DOI":"10.1109\/ICDMW.2019.00166"},{"issue":"3","key":"32_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3046791","volume":"11","author":"P Rozenshtein","year":"2017","unstructured":"Rozenshtein, P., Tatti, N., Gionis, A.: Finding dynamic dense subgraphs. TKDD 11(3), 1\u201330 (2017)","journal-title":"TKDD"},{"issue":"5","key":"32_CR16","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s10618-018-0602-x","volume":"33","author":"K Semertzidis","year":"2018","unstructured":"Semertzidis, K., Pitoura, E., Terzi, E., Tsaparas, P.: Finding lasting dense subgraphs. Data Min. Knowl. Disc. 33(5), 1417\u20131445 (2018). https:\/\/doi.org\/10.1007\/s10618-018-0602-x","journal-title":"Data Min. Knowl. Disc."},{"key":"32_CR17","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Parallelizing maximal clique and k-plex enumeration over graph data. J. Parallel Distr. Comput. 106, 79\u201391 (2017)","DOI":"10.1016\/j.jpdc.2017.03.003"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Yu, W., Aggarwal, C.C., Ma, S., Wang, H.: On anomalous hotspot discovery in graph streams. In: ICDM (2013)","DOI":"10.1109\/ICDM.2013.32"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Zhang, J., Feigenbaum, J.: Finding highly correlated pairs efficiently with powerful pruning. In: CIKM (2006)","DOI":"10.1145\/1183614.1183640"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-75762-5_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T17:18:08Z","timestamp":1710350288000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-75762-5_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030757618","9783030757625"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-75762-5_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"9 May 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 May 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 May 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pakdd2021.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"673","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":"157","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":"23% - 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":"7","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)"}}]}}