{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T16:28:37Z","timestamp":1773246517953,"version":"3.50.1"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031434174","type":"print"},{"value":"9783031434181","type":"electronic"}],"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\/978-3-031-43418-1_16","type":"book-chapter","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T09:02:26Z","timestamp":1694854946000},"page":"259-276","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Quantifying Node-Based Core Resilience"],"prefix":"10.1007","author":[{"given":"Jakir","family":"Hossain","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sucheta","family":"Soundarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahmet Erdem","family":"Sar\u0131y\u00fcce","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,17]]},"reference":[{"key":"16_CR1","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1007\/978-3-642-40988-2_35","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Adiga","year":"2013","unstructured":"Adiga, A., Vullikanti, A.K.S.: How robust is the core of a network? In: Blockeel, H., Kersting, K., Nijssen, S., \u017delezn\u00fd, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8188, pp. 541\u2013556. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40988-2_35"},{"key":"16_CR2","first-page":"498","volume":"14","author":"M Altaf-Ul-Amine","year":"2003","unstructured":"Altaf-Ul-Amine, M., et al.: Prediction of protein functions based on k-cores of protein-protein interaction networks and amino acid sequences. Genome Inf. 14, 498\u2013499 (2003)","journal-title":"Genome Inf."},{"key":"16_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-540-95995-3_3","volume-title":"Algorithms and Models for the Web-Graph","author":"R Andersen","year":"2009","unstructured":"Andersen, R., Chellapilla, K.: Finding dense subgraphs with size bounds. In: Avrachenkov, K., Donato, D., Litvak, N. (eds.) WAW 2009. LNCS, vol. 5427, pp. 25\u201337. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-540-95995-3_3"},{"key":"16_CR4","volume-title":"Infectious Diseases of Humans: Dynamics and Control","author":"RM Anderson","year":"1992","unstructured":"Anderson, R.M., May, R.M.: Infectious Diseases of Humans: Dynamics and Control. Oxford University Press, Oxford (1992)"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone\u2019s an influencer: quantifying influence on twitter. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 65\u201374 (2011)","DOI":"10.1145\/1935826.1935845"},{"issue":"1","key":"16_CR6","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1287\/opre.1100.0851","volume":"59","author":"B Balasundaram","year":"2011","unstructured":"Balasundaram, B., Butenko, S., Hicks, I.V.: Clique relaxations in social network analysis: the maximum k-plex problem. Oper. Res. 59(1), 133\u2013142 (2011)","journal-title":"Oper. Res."},{"key":"16_CR7","unstructured":"Batagelj, V., Zaversnik, M.: An O(m) algorithm for cores decomposition of networks. corr. arXiv preprint cs.DS\/0310049 37 (2003)"},{"issue":"2","key":"16_CR8","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s11634-010-0079-y","volume":"5","author":"V Batagelj","year":"2011","unstructured":"Batagelj, V., Zaversnik, M.: Fast algorithms for determining (generalized) core groups in social networks. Adv. Data Anal. Classif. 5(2), 129\u2013145 (2011)","journal-title":"Adv. Data Anal. Classif."},{"issue":"3","key":"16_CR9","doi-asserted-by":"publisher","first-page":"1452","DOI":"10.1137\/14097032X","volume":"29","author":"K Bhawalkar","year":"2015","unstructured":"Bhawalkar, K., Kleinberg, J., Lewi, K., Roughgarden, T., Sharma, A.: Preventing unraveling in social networks: the anchored k-core problem. SIAM J. Disc. Math. 29(3), 1452\u20131475 (2015)","journal-title":"SIAM J. Disc. Math."},{"issue":"1","key":"16_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-59959-4","volume":"10","author":"K Burleson-Lesser","year":"2020","unstructured":"Burleson-Lesser, K., Morone, F., Tomassone, M.S., Makse, H.A.: K-core robustness in ecological and financial networks. Sci. Rep. 10(1), 1\u201314 (2020)","journal-title":"Sci. Rep."},{"issue":"27","key":"16_CR11","doi-asserted-by":"publisher","first-page":"11150","DOI":"10.1073\/pnas.0701175104","volume":"104","author":"S Carmi","year":"2007","unstructured":"Carmi, S., Havlin, S., Kirkpatrick, S., Shavitt, Y., Shir, E.: A model of internet topology using k-shell decomposition. Proc. Natl. Acad. Sci. 104(27), 11150\u201311154 (2007)","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"21","key":"16_CR12","doi-asserted-by":"publisher","first-page":"218701","DOI":"10.1103\/PhysRevLett.105.218701","volume":"105","author":"C Castellano","year":"2010","unstructured":"Castellano, C., Pastor-Satorras, R.: Thresholds for epidemic spreading in networks. Phys. Rev. Lett. 105(21), 218701 (2010)","journal-title":"Phys. Rev. Lett."},{"issue":"4","key":"16_CR13","doi-asserted-by":"publisher","first-page":"1777","DOI":"10.1016\/j.physa.2011.09.017","volume":"391","author":"D Chen","year":"2012","unstructured":"Chen, D., L\u00fc, L., Shang, M.S., Zhang, Y.C., Zhou, T.: Identifying influential nodes in complex networks. Physica A: Stat. Mech. Appl. 391(4), 1777\u20131787 (2012)","journal-title":"Physica A: Stat. Mech. Appl."},{"key":"16_CR14","unstructured":"Dey, P., Maity, S.K., Medya, S., Silva, A.: Network robustness via global k-cores. In: Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems, pp. 438\u2013446 (2021)"},{"issue":"4","key":"16_CR15","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1145\/316194.316229","volume":"29","author":"M Faloutsos","year":"1999","unstructured":"Faloutsos, M., Faloutsos, P., Faloutsos, C.: On power-law relationships of the internet topology. ACM SIGCOMM Comput. Commun. Rev. 29(4), 251\u2013262 (1999)","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"issue":"2","key":"16_CR16","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s10115-012-0539-0","volume":"35","author":"C Giatsidis","year":"2013","unstructured":"Giatsidis, C., Thilikos, D.M., Vazirgiannis, M.: D-cores: measuring collaboration of directed graphs based on degeneracy. Knowl. Inf. Syst. 35(2), 311\u2013343 (2013)","journal-title":"Knowl. Inf. Syst."},{"issue":"5","key":"16_CR17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.73.056101","volume":"73","author":"AV Goltsev","year":"2006","unstructured":"Goltsev, A.V., Dorogovtsev, S.N., Mendes, J.F.F.: k-core (bootstrap) percolation on complex networks: critical phenomena and nonlocal effects. Phys. Rev. E 73(5), 056101 (2006)","journal-title":"Phys. Rev. E"},{"issue":"2","key":"16_CR18","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1145\/2503792.2503797","volume":"42","author":"A Guille","year":"2013","unstructured":"Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM Sigmod Rec. 42(2), 17\u201328 (2013)","journal-title":"ACM Sigmod Rec."},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Hossain, J., Soundarajan, S., Sar\u0131y\u00fcce, A.E.: Quantifying node-based core resilience. arXiv preprint arXiv:2306.12038 (2023)","DOI":"10.1007\/978-3-031-43418-1_16"},{"issue":"11","key":"16_CR20","doi-asserted-by":"publisher","first-page":"888","DOI":"10.1038\/nphys1746","volume":"6","author":"M Kitsak","year":"2010","unstructured":"Kitsak, M., et al.: Identification of influential spreaders in complex networks. Nat. Phys. 6(11), 888\u2013893 (2010)","journal-title":"Nat. Phys."},{"issue":"2","key":"16_CR21","doi-asserted-by":"publisher","first-page":"222","DOI":"10.1006\/jagm.1994.1032","volume":"17","author":"G Kortsarz","year":"1994","unstructured":"Kortsarz, G., Peleg, D.: Generating sparse 2-spanners. J. Algor. 17(2), 222\u2013236 (1994)","journal-title":"J. Algor."},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Laishram, R., Sariy\u00fcce, A.E., Eliassi-Rad, T., Pinar, A., Soundarajan, S.: Measuring and improving the core resilience of networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 609\u2013618 (2018)","DOI":"10.1145\/3178876.3186127"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Laishram, R., Sariyuce, A.E., Eliassi-Rad, T., Pinar, A., Soundarajan, S.: Residual core maximization: an efficient algorithm for maximizing the size of the k-core. In: Proceedings of the 2020 SIAM International Conference on Data Mining, pp. 325\u2013333. SIAM (2020)","DOI":"10.1137\/1.9781611976236.37"},{"issue":"1","key":"16_CR24","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1145\/3519262","volume":"66","author":"TG Lewis","year":"2022","unstructured":"Lewis, T.G.: The many faces of resilience. Commun. ACM 66(1), 56\u201361 (2022)","journal-title":"Commun. ACM"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, pp. 556\u2013559 (2003)","DOI":"10.1145\/956863.956972"},{"issue":"45","key":"16_CR26","doi-asserted-by":"publisher","first-page":"3279","DOI":"10.1016\/j.physleta.2014.09.054","volume":"378","author":"JH Lin","year":"2014","unstructured":"Lin, J.H., Guo, Q., Dong, W.Z., Tang, L.Y., Liu, J.G.: Identifying the node spreading influence with largest k-core values. Phys. Lett. A 378(45), 3279\u20133284 (2014)","journal-title":"Phys. Lett. A"},{"issue":"4","key":"16_CR27","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/j.cnsns.2013.08.028","volume":"19","author":"C Liu","year":"2014","unstructured":"Liu, C., Zhang, Z.K.: Information spreading on dynamic social networks. Commun. Nonlinear Sci. Numer. Simul. 19(4), 896\u2013904 (2014)","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"issue":"3","key":"16_CR28","doi-asserted-by":"publisher","first-page":"38005","DOI":"10.1209\/0295-5075\/88\/38005","volume":"88","author":"M Medo","year":"2009","unstructured":"Medo, M., Zhang, Y.C., Zhou, T.: Adaptive model for recommendation of news. EPL (Europhys. Lett.) 88(3), 38005 (2009)","journal-title":"EPL (Europhys. Lett.)"},{"key":"16_CR29","doi-asserted-by":"crossref","unstructured":"Medya, S., Ma, T., Silva, A., Singh, A.: A game theoretic approach for core resilience. In: International Joint Conferences on Artificial Intelligence Organization (2020)","DOI":"10.24963\/ijcai.2020\/480"},{"issue":"2","key":"16_CR30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.64.025102","volume":"64","author":"ME Newman","year":"2001","unstructured":"Newman, M.E.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)","journal-title":"Phys. Rev. E"},{"key":"16_CR31","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.cor.2019.02.006","volume":"106","author":"D Purevsuren","year":"2019","unstructured":"Purevsuren, D., Cui, G.: Efficient heuristic algorithm for identifying critical nodes in planar networks. Comput. Oper. Res. 106, 143\u2013153 (2019)","journal-title":"Comput. Oper. Res."},{"issue":"6","key":"16_CR32","doi-asserted-by":"publisher","first-page":"433","DOI":"10.14778\/2536336.2536344","volume":"6","author":"AE Sariy\u00fcce","year":"2013","unstructured":"Sariy\u00fcce, A.E., Gedik, B., Jacques-Silva, G., Wu, K.L., \u00c7ataly\u00fcrek, \u00dc.V.: Streaming algorithms for k-core decomposition. Proc. VLDB Endow. 6(6), 433\u2013444 (2013)","journal-title":"Proc. VLDB Endow."},{"issue":"3","key":"16_CR33","doi-asserted-by":"publisher","first-page":"425","DOI":"10.1007\/s00778-016-0423-8","volume":"25","author":"AE Sar\u0131y\u00fcce","year":"2016","unstructured":"Sar\u0131y\u00fcce, A.E., Gedik, B., Jacques-Silva, G., Wu, K.L., \u00c7ataly\u00fcrek, \u00dc.V.: Incremental k-core decomposition: algorithms and evaluation. VLDB J. 25(3), 425\u2013447 (2016)","journal-title":"VLDB J."},{"issue":"2","key":"16_CR34","doi-asserted-by":"publisher","first-page":"cnab018","DOI":"10.1093\/comnet\/cnab018","volume":"9","author":"SE Schaeffer","year":"2021","unstructured":"Schaeffer, S.E., Vald\u00e9s, V., Figols, J., Bachmann, I., Morales, F., Bustos-Jim\u00e9nez, J.: Characterization of robustness and resilience in graphs: a mini-review. J. Complex Netw. 9(2), cnab018 (2021)","journal-title":"J. Complex Netw."},{"issue":"5","key":"16_CR35","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.051911","volume":"82","author":"DJ Schwab","year":"2010","unstructured":"Schwab, D.J., Bruinsma, R.F., Feldman, J.L., Levine, A.J.: Rhythmogenic neuronal networks, emergent leaders, and k-cores. Phys. Rev. E 82(5), 051911 (2010)","journal-title":"Phys. Rev. E"},{"issue":"3","key":"16_CR36","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/0378-8733(83)90028-X","volume":"5","author":"SB Seidman","year":"1983","unstructured":"Seidman, S.B.: Network structure and minimum degree. Social Netw. 5(3), 269\u2013287 (1983)","journal-title":"Social Netw."},{"issue":"4","key":"16_CR37","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.tpb.2011.01.004","volume":"79","author":"KJ Sharkey","year":"2011","unstructured":"Sharkey, K.J.: Deterministic epidemic models on contact networks: correlations and unbiological terms. Theor. Popul. Biol. 79(4), 115\u2013129 (2011)","journal-title":"Theor. Popul. Biol."},{"issue":"7","key":"16_CR38","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.14778\/3523210.3523214","volume":"15","author":"X Sun","year":"2022","unstructured":"Sun, X., Huang, X., Jin, D.: Fast algorithms for core maximization on large graphs. Proc. VLDB Endow. 15(7), 1350\u20131362 (2022)","journal-title":"Proc. VLDB Endow."},{"key":"16_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2020.124229","volume":"554","author":"M Wang","year":"2020","unstructured":"Wang, M., Li, W., Guo, Y., Peng, X., Li, Y.: Identifying influential spreaders in complex networks based on improved k-shell method. Physica A: Stat. Mech. Appl. 554, 124229 (2020)","journal-title":"Physica A: Stat. Mech. Appl."},{"key":"16_CR40","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.eswa.2017.10.018","volume":"93","author":"A Zareie","year":"2018","unstructured":"Zareie, A., Sheikhahmadi, A.: A hierarchical approach for influential node ranking in complex social networks. Expert Syst. Appl. 93, 200\u2013211 (2018)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"16_CR41","doi-asserted-by":"publisher","first-page":"649","DOI":"10.14778\/3055330.3055332","volume":"10","author":"F Zhang","year":"2017","unstructured":"Zhang, F., Zhang, W., Zhang, Y., Qin, L., Lin, X.: OLAK: an efficient algorithm to prevent unraveling in social networks. Proc. VLDB Endow. 10(6), 649\u2013660 (2017)","journal-title":"Proc. VLDB Endow."},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, F., Zhang, Y., Qin, L., Zhang, W., Lin, X.: Finding critical users for social network engagement: the collapsed k-core problem. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)","DOI":"10.1609\/aaai.v31i1.10482"},{"issue":"1","key":"16_CR43","first-page":"456","volume":"35","author":"K Zhao","year":"2023","unstructured":"Zhao, K., Zhang, Z., Rong, Y., Yu, J.X., Huang, J.: Finding critical users in social communities via graph convolutions. IEEE Trans. Knowl. Data Eng. 35(1), 456\u2013468 (2023)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"3","key":"16_CR44","first-page":"1797","volume":"69","author":"B Zhou","year":"2021","unstructured":"Zhou, B., Lv, Y., Mao, Y., Wang, J., Yu, S., Xuan, Q.: The robustness of graph k-shell structure under adversarial attacks. IEEE Trans. Circ. Syst. II: Express Briefs 69(3), 1797\u20131801 (2021)","journal-title":"IEEE Trans. Circ. Syst. II: Express Briefs"},{"key":"16_CR45","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Zhang, F., Lin, X., Zhang, W., Chen, C.: K-core maximization: an edge addition approach. In: IJCAI, pp. 4867\u20134873 (2019)","DOI":"10.24963\/ijcai.2019\/676"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"Zhu, W., Chen, C., Wang, X., Lin, X.: K-core minimization: an edge manipulation approach. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1667\u20131670 (2018)","DOI":"10.1145\/3269206.3269254"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases: Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43418-1_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T13:07:20Z","timestamp":1719407240000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43418-1_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434174","9783031434181"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43418-1_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"Our contribution is algorithmic in nature, building on previously proposed concepts. We work on public datasets. We do not foresee any ethical implications of our work.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"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":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"24% - 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":"3.63","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":"4.5","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":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","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)"}}]}}