{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T08:15:00Z","timestamp":1759133700811,"version":"3.40.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T00:00:00Z","timestamp":1669766400000},"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":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s11390-022-2367-3","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:02:56Z","timestamp":1672290176000},"page":"1337-1355","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["I\/O Efficient Early Bursting Cohesive Subgraph Discovery in Massive Temporal Networks"],"prefix":"10.1007","volume":"37","author":[{"given":"Yuan","family":"Li","sequence":"first","affiliation":[]},{"given":"Jie","family":"Dai","sequence":"additional","affiliation":[]},{"given":"Xiao-Lin","family":"Fan","sequence":"additional","affiliation":[]},{"given":"Yu-Hai","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Guo-Ren","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"issue":"3","key":"2367_CR1","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.physrep.2012.03.001","volume":"519","author":"P Holme","year":"2012","unstructured":"Holme P, Saram\u00e4ki J. Temporal networks. Physics Reports, 2012, 519(3): 97-125. DOI: https:\/\/doi.org\/10.1016\/j.physrep.2012.03.001.","journal-title":"Physics Reports"},{"key":"2367_CR2","doi-asserted-by":"crossref","unstructured":"Li R H, Su J, Qin L, Yu J X, Dai Q. Persistent community search in temporal networks. In Proc. the 34th IEEE International Conference on Data Engineering, Apr. 2018, pp.797-808. DOI: 10.1109\/ICDE.2018.00077.","DOI":"10.1109\/ICDE.2018.00077"},{"issue":"5","key":"2367_CR3","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s10618-018-0602-x","volume":"33","author":"K Semertzidis","year":"2019","unstructured":"Semertzidis K, Pitoura E, Terzi E, Tsaparas P. Finding lasting dense subgraphs. Data Min. Knowl. Discov., 2019, 33(5): 1417-1445. DOI: https:\/\/doi.org\/10.1007\/s10618-018-0602-x.","journal-title":"Data Min. Knowl. Discov."},{"issue":"3","key":"2367_CR4","doi-asserted-by":"publisher","first-page":"671","DOI":"10.1109\/TBDATA.2020.2974849","volume":"8","author":"H Qin","year":"2022","unstructured":"Qin H, Li R H, Wang G, Huang X, Yuan Y, Yu J X. Mining stable communities in temporal networks by density-based clustering. IEEE Trans. Big Data, 2022, 8(3): 671-684. DOI: https:\/\/doi.org\/10.1109\/TBDATA.2020.2974849.","journal-title":"IEEE Trans. Big Data"},{"key":"2367_CR5","doi-asserted-by":"publisher","unstructured":"Lin L, Yuan P, Li R, Jin H. Mining diversified top-r lasting cohesive subgraphs on temporal networks. IEEE Transactions on Big Data. DOI: https:\/\/doi.org\/10.1109\/TBDATA.2021.3058294.","DOI":"10.1109\/TBDATA.2021.3058294"},{"issue":"5","key":"2367_CR6","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1007\/s11280-021-00917-z","volume":"24","author":"Y Li","year":"2021","unstructured":"Li Y, Liu J, Zhao H, Sun J, Zhao Y, Wang G. Efficient continual cohesive subgraph search in large temporal graphs. World Wide Web, 2021, 24(5): 1483-1509. DOI: https:\/\/doi.org\/10.1007\/s11280-021-00917-z.","journal-title":"World Wide Web"},{"key":"2367_CR7","doi-asserted-by":"crossref","unstructured":"Qin H, Li R H, Wang G, Qin L, Cheng Y, Yuan Y. Mining periodic cliques in temporal networks. In Proc. the 35th IEEE International Conference on Data Engineering, Apr. 2019, pp.1130-1141. DOI: 10.1109\/ICDE.2019.00104.","DOI":"10.1109\/ICDE.2019.00104"},{"key":"2367_CR8","doi-asserted-by":"crossref","unstructured":"Zhang Q, Guo D, Zhao X, Li X, Wang X. Seasonal-periodic subgraph mining in temporal networks. In Proc. the 29th ACM International Conference on Information and Knowledge Management, Oct. 2020, pp.2309-2312. DOI: 10.1145\/3340531.3412091.","DOI":"10.1145\/3340531.3412091"},{"key":"2367_CR9","unstructured":"Qin H, Li R H, Wang G, Qin L, Yuan Y, Zhang Z. Mining bursting communities in temporal graphs. arXiv:191-1.02780, 2019. https:\/\/arxiv.org\/abs\/1911.02780, Jul. 2022."},{"issue":"13","key":"2367_CR10","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.14778\/3358701.3358704","volume":"12","author":"L Chu","year":"2019","unstructured":"Chu L, Zhang Y, Yang Y, Wang L, Pei J. Online density bursting subgraph detection from temporal graphs. Proc. VLDB Endow., 2019, 12(13): 2353-2365. DOI: https:\/\/doi.org\/10.14778\/3358701.3358704.","journal-title":"Proc. VLDB Endow."},{"key":"2367_CR11","doi-asserted-by":"crossref","unstructured":"Palen L, Hughes A L. Social media in disaster communication. In Handbook of Disaster Research, Rodr\u00edguez H, Donner W, Trainor J E (eds.), Springer Cham, 2018, pp.497-518. DOI: 10.1007\/978-3-319-63254-4 24.","DOI":"10.1007\/978-3-319-63254-4_24"},{"key":"2367_CR12","doi-asserted-by":"publisher","unstructured":"Jain V, Sharma A, Subramanian L. Road traffic congestion in the developing world. In Proc. the 2nd ACM Symposium on Computing for Development, Mar. 2012, Article No. 11. DOI: https:\/\/doi.org\/10.1145\/2160601.2160616.","DOI":"10.1145\/2160601.2160616"},{"key":"2367_CR13","doi-asserted-by":"publisher","unstructured":"Cooper I, Mondal A, Antonopoulos G C. A SIR model assumption for the spread of COVID-19 in different communities. Chaos, Solitons & Fractals, 2020, 139: Article No. 110057. DOI: https:\/\/doi.org\/10.1016\/j.chaos.2020.110057.","DOI":"10.1016\/j.chaos.2020.110057"},{"issue":"5","key":"2367_CR14","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.1007\/s10618-015-0422-1","volume":"29","author":"N Barbieri","year":"2015","unstructured":"Barbieri N, Bonchi F, Galimberti E, Gullo F. Efficient and effective community search. Data Min. Knowl. Discov., 2015, 29(5): 1406-1433. DOI: https:\/\/doi.org\/10.1007\/s10618-015-0422-1.","journal-title":"Data Min. Knowl. Discov."},{"key":"2367_CR15","doi-asserted-by":"crossref","unstructured":"Cui W, Xiao Y, Wang H, Wang W. Local search of communities in large graphs. In Proc. the 2014 ACM SIGMOD International Conference on Management of Data, Jun. 2014, pp.991-1002. DOI: 10.1145\/2588555.2612179.","DOI":"10.1145\/2588555.2612179"},{"key":"2367_CR16","doi-asserted-by":"publisher","unstructured":"Dai J, Li Y, Fan X, Sun J, Zhao Y. Finding early bursting cohesive subgraphs in large temporal networks. In Proc. the 2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation, Oct. 2021, pp.264-271. DOI: https:\/\/doi.org\/10.1109\/SWC50871.2021.00044.","DOI":"10.1109\/SWC50871.2021.00044"},{"issue":"5","key":"2367_CR17","doi-asserted-by":"publisher","first-page":"509","DOI":"10.14778\/2735479.2735484","volume":"8","author":"RH Li","year":"2015","unstructured":"Li R H, Qin L, Yu J X, Mao R. Influential community search in large networks. Proc. VLDB Endow., 2015, 8(5): 509-520. DOI: https:\/\/doi.org\/10.14778\/2735479.2735484.","journal-title":"Proc. VLDB Endow."},{"issue":"6","key":"2367_CR18","doi-asserted-by":"publisher","first-page":"751","DOI":"10.1007\/s00778-017-0467-4","volume":"26","author":"R Li","year":"2017","unstructured":"Li R, Qin L, Yu J X, Mao R. Finding influential communities in massive networks. VLDB J., 2017, 26(6): 751-776. DOI: https:\/\/doi.org\/10.1007\/s00778-017-0467-4.","journal-title":"VLDB J."},{"key":"2367_CR19","doi-asserted-by":"crossref","unstructured":"Chen S, Wei R, Popova D, Thomo A. Efficient computation of importance based communities in web-scale networks using a single machine. In Proc. the 25th ACM International Conference on Information and Knowledge Management, Oct. 2016, pp.1553-1562. DOI: 10.1145\/2983323.2983836.","DOI":"10.1145\/2983323.2983836"},{"issue":"9","key":"2367_CR20","doi-asserted-by":"publisher","first-page":"1056","DOI":"10.14778\/3213880.3213881","volume":"11","author":"F Bi","year":"2018","unstructured":"Bi F, Chang L, Lin X, Zhang W. An optimal and progressive approach to online search of top-k influential communities. Proc. VLDB Endow., 2018, 11(9): 1056-1068. DOI: https:\/\/doi.org\/10.14778\/3213880.3213881.","journal-title":"Proc. VLDB Endow."},{"key":"2367_CR21","doi-asserted-by":"publisher","first-page":"344","DOI":"10.1016\/j.ins.2017.07.012","volume":"417","author":"Z Zheng","year":"2017","unstructured":"Zheng Z, Ye F, Li R H, Ling G, Jin T. Finding weighted k-truss communities in large networks. Inf. Sci., 2017, 417: 344-360. DOI: https:\/\/doi.org\/10.1016\/j.ins.2017.07.012.","journal-title":"Inf. Sci."},{"issue":"9","key":"2367_CR22","doi-asserted-by":"publisher","first-page":"4313","DOI":"10.1109\/TKDE.2020.3040762","volume":"34","author":"L Sun","year":"2022","unstructured":"Sun L, Huang X, Li R, Choi B, Xu J. Index-based intimatecore community search in large weighted graphs. IEEE Trans. Knowl. Data Eng., 2022, 34(9): 4313-4327. DOI: https:\/\/doi.org\/10.1109\/TKDE.2020.3040762.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2367_CR23","doi-asserted-by":"crossref","unstructured":"Lahiri M, Berger-Wolf T F. Mining periodic behavior in dynamic social networks. In Proc. the 8th IEEE International Conference on Data Mining, Dec. 2008, pp.373-382. DOI: 10.1109\/ICDM.2008.104.","DOI":"10.1109\/ICDM.2008.104"},{"issue":"8","key":"2367_CR24","doi-asserted-by":"publisher","first-page":"3927","DOI":"10.1109\/TKDE.2020.3028025","volume":"34","author":"H Qin","year":"2022","unstructured":"Qin H, Li R, Yuan Y, Wang G, Yang W, Qin L. Periodic communities mining in temporal networks: Concepts and algorithms. IEEE Trans. Knowl. Data Eng., 2022, 34(8): 3927-3945. DOI: DOI: https:\/\/doi.org\/10.1109\/TKDE.2020.3028025.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2367_CR25","doi-asserted-by":"publisher","unstructured":"Maheshwari A, Zeh N. A survey of techniques for designing I\/O-efficient algorithms. In Algorithms for Memory Hierarchies, Meyer U, Sanders P, Sibeyn J (eds.), Springer, 2003, pp.36-61. DOI: https:\/\/doi.org\/10.1007\/3-540-36574-5_3.","DOI":"10.1007\/3-540-36574-5_3"},{"key":"2367_CR26","doi-asserted-by":"crossref","unstructured":"Cheng J, Ke Y, Chu S, \u00d6zsu M. Efficient core decomposition in massive networks. In Proc. the 27th IEEE International Conference on Data Engineering, Apr. 2011, pp.51-62. DOI: 10.1109\/ICDE.2011.5767911.","DOI":"10.1109\/ICDE.2011.5767911"},{"issue":"4","key":"2367_CR27","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1109\/TBDATA.2019.2908384","volume":"6","author":"P Sun","year":"2020","unstructured":"Sun P, Wen Y, Duong T N B, Xiao X. GraphMP: I\/Oe efficient big graph analytics on a single commodity machine. IEEE Trans. Big Data, 2020, 6(4): 816-829. DOI: https:\/\/doi.org\/10.1109\/TBDATA.2019.2908384.","journal-title":"IEEE Trans. Big Data"},{"key":"2367_CR28","doi-asserted-by":"crossref","unstructured":"Wen D, Qin L, Zhang Y, Lin X, Yu J X. I\/O efficient core graph decomposition at web scale. In Proc. the 32nd IEEE International Conference on Data Engineering, May 2016, pp.133-144. DOI: 10.1109\/ICDE.2016.7498235.","DOI":"10.1109\/ICDE.2016.7498235"},{"issue":"2","key":"2367_CR29","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/s00778-016-0451-4","volume":"26","author":"L Yuan","year":"2017","unstructured":"Yuan L, Qin L, Lin X, Chang L, Zhang W. I\/O efficient ECC graph decomposition via graph reduction. VLDB J., 2017, 26(2): 275-300. DOI: https:\/\/doi.org\/10.1007\/s00778-016-0451-4.","journal-title":"VLDB J."},{"issue":"2","key":"2367_CR30","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1007\/s00778-014-0372-z","volume":"24","author":"Z Zhang","year":"2015","unstructured":"Zhang Z, Yu J X, Qin L, Chang L, Lin X. I\/O efficient: Computing SCCs in massive graphs. VLDB J., 2015, 24(2): 245-270. DOI: https:\/\/doi.org\/10.1007\/s00778-014-0372-z.","journal-title":"VLDB J."},{"issue":"5","key":"2367_CR31","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1007\/s00778-020-00649-y","volume":"30","author":"Y Jiang","year":"2021","unstructured":"Jiang Y, Huang X, Cheng H. I\/O efficient k-truss community search in massive graphs. VLDB J., 2021, 30(5): 713-738. DOI: https:\/\/doi.org\/10.1007\/s00778-020-00649-y.","journal-title":"VLDB J."},{"issue":"2","key":"2367_CR32","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1007\/s11280-019-00725-6","volume":"23","author":"Y Li","year":"2020","unstructured":"Li Y, Wang G, Zhao Y, Zhu F, Wu Y. Towards k-vertex connected component discovery from large networks. World Wide Web, 2020, 23(2): 799-830. DOI: https:\/\/doi.org\/10.1007\/s11280-019-00725-6.","journal-title":"World Wide Web"},{"issue":"9","key":"2367_CR33","doi-asserted-by":"publisher","first-page":"1721","DOI":"10.11897\/SP.J.1016.2020.01721","volume":"43","author":"Y Li","year":"2020","unstructured":"Li Y, Sheng F, Sun J, Zhao Y, Wang G. A k-connected truss subgraph discovery algorithm in large scale dual networks. Chinese Journal of Computers, 2020, 43(9): 1721-1736. DOI: https:\/\/doi.org\/10.11897\/SP.J.1016.2020.01721. (in Chinese)","journal-title":"Chinese Journal of Computers"}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-022-2367-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11390-022-2367-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-022-2367-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,9]],"date-time":"2025-04-09T15:00:34Z","timestamp":1744210834000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11390-022-2367-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,30]]},"references-count":33,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["2367"],"URL":"https:\/\/doi.org\/10.1007\/s11390-022-2367-3","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"type":"print","value":"1000-9000"},{"type":"electronic","value":"1860-4749"}],"subject":[],"published":{"date-parts":[[2022,11,30]]},"assertion":[{"value":"30 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}