{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:52:43Z","timestamp":1743025963508,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819923557"},{"type":"electronic","value":"9789819923564"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-2356-4_4","type":"book-chapter","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:02:49Z","timestamp":1683896569000},"page":"42-57","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Incremental Evolutionary Community Discovery Method Based on\u00a0Neighbor Subgraph"],"prefix":"10.1007","author":[{"given":"Yan","family":"Zhao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingmei","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,13]]},"reference":[{"issue":"6","key":"4_CR1","doi-asserted-by":"publisher","first-page":"1287","DOI":"10.1086\/228667","volume":"92","author":"RS Burt","year":"1987","unstructured":"Burt, R.S.: Social contagion and innovation: cohesion versus structural equivalence. Am. J. Sociol. 92(6), 1287\u20131335 (1987)","journal-title":"Am. J. Sociol."},{"issue":"2","key":"4_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2532549","volume":"5","author":"YC Chen","year":"2014","unstructured":"Chen, Y.C., Zhu, W.Y., Peng, W.C., Lee, W.C., Lee, S.Y.: CIM: community-based influence maximization in social networks. ACM Trans. Intell. Syst. Technol. (TIST) 5(2), 1\u201331 (2014)","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"12","key":"4_CR3","doi-asserted-by":"publisher","first-page":"7821","DOI":"10.1073\/pnas.122653799","volume":"99","author":"M Girvan","year":"2002","unstructured":"Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821\u20137826 (2002)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"10","key":"4_CR4","doi-asserted-by":"publisher","first-page":"103018","DOI":"10.1088\/1367-2630\/12\/10\/103018","volume":"12","author":"S Gregory","year":"2010","unstructured":"Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)","journal-title":"New J. Phys."},{"issue":"11","key":"4_CR5","doi-asserted-by":"publisher","first-page":"e0188655","DOI":"10.1371\/journal.pone.0188655","volume":"12","author":"J Han","year":"2017","unstructured":"Han, J., Li, W., Zhao, L., Su, Z., Zou, Y., Deng, W.: Community detection in dynamic networks via adaptive label propagation. PLoS ONE 12(11), e0188655 (2017)","journal-title":"PLoS ONE"},{"issue":"11","key":"4_CR6","doi-asserted-by":"publisher","first-page":"2160","DOI":"10.1016\/j.physa.2010.10.040","volume":"390","author":"J Huang","year":"2011","unstructured":"Huang, J., Sun, H., Han, J., Feng, B.: Density-based shrinkage for revealing hierarchical and overlapping community structure in networks. Physica A-Stat. Mech. Appl. 390(11), 2160\u20132171 (2011)","journal-title":"Physica A-Stat. Mech. Appl."},{"key":"4_CR7","doi-asserted-by":"crossref","unstructured":"Kanezashi, H., Suzumura, T.: An incremental local-first community detection method for dynamic graphs. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 3318\u20133325. IEEE (2016)","DOI":"10.1109\/BigData.2016.7840991"},{"issue":"1","key":"4_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/BF01164627","volume":"12","author":"DJ Klein","year":"1993","unstructured":"Klein, D.J., Randi\u0107, M.: Resistance distance. J. Math. Chem. 12(1), 81\u201395 (1993)","journal-title":"J. Math. Chem."},{"issue":"3","key":"4_CR9","doi-asserted-by":"publisher","first-page":"033015","DOI":"10.1088\/1367-2630\/11\/3\/033015","volume":"11","author":"A Lancichinetti","year":"2009","unstructured":"Lancichinetti, A., Fortunato, S., Kert\u00e9sz, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys. 11(3), 033015 (2009)","journal-title":"New J. Phys."},{"key":"4_CR10","doi-asserted-by":"crossref","unstructured":"Li, G., Guo, K., Chen, Y., Wu, L., Zhu, D.: A dynamic community detection algorithm based on parallel incremental related vertices. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 779\u2013783. IEEE (2017)","DOI":"10.1109\/ICBDA.2017.8078743"},{"issue":"2","key":"4_CR11","doi-asserted-by":"publisher","first-page":"102818","DOI":"10.1016\/j.ipm.2021.102818","volume":"59","author":"W Li","year":"2022","unstructured":"Li, W., Li, Y., Liu, W., Wang, C.: An influence maximization method based on crowd emotion under an emotion-based attribute social network. Inf. Process. Manage. 59(2), 102818 (2022)","journal-title":"Inf. Process. Manage."},{"key":"4_CR12","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.ins.2021.04.061","volume":"568","author":"W Li","year":"2021","unstructured":"Li, W., Li, Z., Luvembe, A.M., Yang, C.: Influence maximization algorithm based on gaussian propagation model. Inf. Sci. 568, 386\u2013402 (2021)","journal-title":"Inf. Sci."},{"key":"4_CR13","doi-asserted-by":"publisher","first-page":"109673","DOI":"10.1016\/j.knosys.2022.109673","volume":"255","author":"W Li","year":"2022","unstructured":"Li, W., Ni, L., Wang, J., Wang, C.: Collaborative representation learning for nodes and relations via heterogeneous graph neural network. Knowl.-Based Syst. 255, 109673 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"4_CR14","doi-asserted-by":"publisher","first-page":"114207","DOI":"10.1016\/j.eswa.2020.114207","volume":"169","author":"W Li","year":"2021","unstructured":"Li, W., Zhong, K., Wang, J., Chen, D.: A dynamic algorithm based on cohesive entropy for influence maximization in social networks. Expert Syst. Appl. 169, 114207 (2021)","journal-title":"Expert Syst. Appl."},{"key":"4_CR15","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1016\/j.inffus.2021.10.002","volume":"79","author":"W Li","year":"2022","unstructured":"Li, W., Zhou, X., Yang, C., Fan, Y., Wang, Z., Liu, Y.: Multi-objective optimization algorithm based on characteristics fusion of dynamic social networks for community discovery. Inf. Fusion 79, 110\u2013123 (2022)","journal-title":"Inf. Fusion"},{"key":"4_CR16","doi-asserted-by":"publisher","first-page":"114536","DOI":"10.1016\/j.eswa.2020.114536","volume":"171","author":"W Li","year":"2021","unstructured":"Li, W., et al.: Evolutionary community discovery in dynamic social networks via resistance distance. Expert Syst. Appl. 171, 114536 (2021)","journal-title":"Expert Syst. Appl."},{"key":"4_CR17","doi-asserted-by":"crossref","unstructured":"Liu, W., Suzumura, T., Chen, L., Hu, G.: A generalized incremental bottom-up community detection framework for highly dynamic graphs. In: 2017 IEEE International Conference on Big Data (Big Data), pp. 3342\u20133351. IEEE (2017)","DOI":"10.1109\/BigData.2017.8258319"},{"issue":"4","key":"4_CR18","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1007\/s00265-003-0651-y","volume":"54","author":"D Lusseau","year":"2003","unstructured":"Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396\u2013405 (2003)","journal-title":"Behav. Ecol. Sociobiol."},{"issue":"6","key":"4_CR19","doi-asserted-by":"publisher","first-page":"066133","DOI":"10.1103\/PhysRevE.69.066133","volume":"69","author":"ME Newman","year":"2004","unstructured":"Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)","journal-title":"Phys. Rev. E"},{"issue":"8","key":"4_CR20","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1007\/s10994-016-5582-8","volume":"106","author":"G Rossetti","year":"2017","unstructured":"Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F.: Tiles: an online algorithm for community discovery in dynamic social networks. Mach. Learn. 106(8), 1213\u20131241 (2017)","journal-title":"Mach. Learn."},{"key":"4_CR21","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.knosys.2018.05.026","volume":"157","author":"Z Wang","year":"2018","unstructured":"Wang, Z., Li, Z., Yuan, G., Sun, Y., Rui, X., Xiang, X.: Tracking the evolution of overlapping communities in dynamic social networks. Knowl.-Based Syst. 157, 81\u201397 (2018)","journal-title":"Knowl.-Based Syst."},{"issue":"8","key":"4_CR22","doi-asserted-by":"publisher","DOI":"10.1002\/cpe.5314","volume":"33","author":"L Wu","year":"2021","unstructured":"Wu, L., Zhang, Q., Guo, K., Chen, E., Xu, C.: Dynamic community detection method based on an improved evolutionary matrix. Concurr. Comput. Pract. Experience 33(8), e5314 (2021)","journal-title":"Concurr. Comput. Pract. Experience"},{"key":"4_CR23","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-642-30220-6_3","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"J Xie","year":"2012","unstructured":"Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.-N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS (LNAI), vol. 7302, pp. 25\u201336. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-30220-6_3"},{"key":"4_CR24","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.eswa.2016.03.033","volume":"58","author":"Y Xin","year":"2016","unstructured":"Xin, Y., Xie, Z.Q., Yang, J.: An adaptive random walk sampling method on dynamic community detection. Expert Syst. Appl. 58, 10\u201319 (2016)","journal-title":"Expert Syst. Appl."},{"key":"4_CR25","doi-asserted-by":"publisher","first-page":"112363","DOI":"10.1109\/ACCESS.2019.2935090","volume":"7","author":"C Xue","year":"2019","unstructured":"Xue, C., Wu, S., Zhang, Q., Shao, F.: An incremental group-specific framework based on community detection for cold start recommendation. IEEE Access 7, 112363\u2013112374 (2019)","journal-title":"IEEE Access"},{"issue":"4","key":"4_CR26","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1086\/jar.33.4.3629752","volume":"33","author":"WW Zachary","year":"1977","unstructured":"Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33(4), 452\u2013473 (1977)","journal-title":"J. Anthropol. Res."},{"key":"4_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, Y., Wu, B.: A parallel community detection algorithm based on incremental clustering in dynamic network. In: 2018 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 946\u2013953. IEEE (2018)","DOI":"10.1109\/ASONAM.2018.8508730"},{"key":"4_CR28","doi-asserted-by":"crossref","unstructured":"Zhuang, D., Chang, M.J., Li, M.: DynaMo: dynamic community detection by incrementally maximizing modularity. IEEE Trans. Knowl. Data Eng. (2019)","DOI":"10.1109\/TKDE.2019.2951419"}],"container-title":["Communications in Computer and Information Science","Computer Supported Cooperative Work and Social Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-2356-4_4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T13:03:17Z","timestamp":1683896597000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-2356-4_4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819923557","9789819923564"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-2356-4_4","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ChineseCSCW","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CCF Conference on Computer Supported Cooperative Work  and Social Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Datong","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"chinesecscw2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conf.scholat.com\/ccscw\/2022","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"211","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":"60","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":"30","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":"28% - 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":"4","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)"}}]}}