{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T18:55:03Z","timestamp":1778266503976,"version":"3.51.4"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031360268","type":"print"},{"value":"9783031360275","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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"content-version":"vor","delay-in-days":176,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Community detection is a fundamental problem in computational sciences with extensive applications in various fields. The most commonly used methods are the algorithms designed to maximize modularity over different partitions of the network nodes. Using 80 real and random networks from a wide range of contexts, we investigate the extent to which current heuristic modularity maximization algorithms succeed in returning maximum-modularity (optimal) partitions. We evaluate (1) the ratio of the algorithms\u2019 output modularity to the maximum modularity for each input graph, and (2) the maximum similarity between their output partition and any optimal partition of that graph. We compare eight existing heuristic algorithms against an exact integer programming method that globally maximizes modularity. The average modularity-based heuristic algorithm returns optimal partitions for only 19.4% of the 80 graphs considered. Additionally, results on adjusted mutual information reveal substantial dissimilarity between the sub-optimal partitions and any optimal partition of the networks in our experiments. More importantly, our results show that near-optimal partitions are often disproportionately dissimilar to any optimal partition. Taken together, our analysis points to a crucial limitation of commonly used modularity-based heuristics for discovering communities: they rarely produce an optimal partition or a partition resembling an optimal partition. If modularity is to be used for detecting communities, exact or approximate optimization algorithms are recommendable for a more methodologically sound usage of modularity within its applicability limits.<\/jats:p>","DOI":"10.1007\/978-3-031-36027-5_48","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T08:02:52Z","timestamp":1688025772000},"page":"612-626","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Heuristic Modularity Maximization Algorithms for\u00a0Community Detection Rarely Return an\u00a0Optimal Partition or\u00a0Anything Similar"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5870-9253","authenticated-orcid":false,"given":"Samin","family":"Aref","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2816-909X","authenticated-orcid":false,"given":"Mahdi","family":"Mostajabdaveh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9049-0255","authenticated-orcid":false,"given":"Hriday","family":"Chheda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"issue":"3","key":"48_CR1","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1140\/epjb\/e2008-00425-1","volume":"66","author":"G Agarwal","year":"2008","unstructured":"Agarwal, G., Kempe, D.: Modularity-maximizing graph communities via mathematical programming. Eur. Phys. J. B 66(3), 409\u2013418 (2008). https:\/\/doi.org\/10.1140\/epjb\/e2008-00425-1","journal-title":"Eur. Phys. J. B"},{"issue":"9","key":"48_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0024195","volume":"6","author":"R Aldecoa","year":"2011","unstructured":"Aldecoa, R., Mar\u00edn, I.: Deciphering network community structure by surprise. PLoS ONE 6(9), 1\u20138 (2011). https:\/\/doi.org\/10.1371\/journal.pone.0024195","journal-title":"PLoS ONE"},{"issue":"4","key":"48_CR3","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.046112","volume":"82","author":"D Aloise","year":"2010","unstructured":"Aloise, D., Cafieri, S., Caporossi, G., Hansen, P., Perron, S., Liberti, L.: Column generation algorithms for exact modularity maximization in networks. Phys. Rev. E 82(4), 046112 (2010). https:\/\/doi.org\/10.1103\/PhysRevE.82.046112","journal-title":"Phys. Rev. E"},{"key":"48_CR4","unstructured":"Aref, S., Chheda, H., Mostajabdaveh, M.: The Bayan algorithm: detecting communities in networks through exact and approximate optimization of modularity. arXiv preprint arXiv:2209.04562 (2022)"},{"key":"48_CR5","doi-asserted-by":"publisher","unstructured":"Aref, S., Chheda, H., Mostajabdaveh, M.: Dataset of networks used in accessing the Bayan algorithm for community detection (2023). https:\/\/doi.org\/10.6084\/m9.figshare.22442785","DOI":"10.6084\/m9.figshare.22442785"},{"issue":"6","key":"48_CR6","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1093\/bioinformatics\/bti098","volume":"21","author":"T Beuming","year":"2005","unstructured":"Beuming, T., Skrabanek, L., Niv, M.Y., Mukherjee, P., Weinstein, H.: PDZBase: a protein-protein interaction database for PDZ-domains. Bioinformatics 21(6), 827\u2013828 (2005)","journal-title":"Bioinformatics"},{"issue":"10","key":"48_CR7","doi-asserted-by":"publisher","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","volume":"2008","author":"VD Blondel","year":"2008","unstructured":"Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008). https:\/\/doi.org\/10.1088\/1742-5468\/2008\/10\/P10008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"48_CR8","unstructured":"Bonald, T., Charpentier, B., Galland, A., Hollocou, A.: Hierarchical graph clustering using node pair sampling. In: MLG 2018\u201314th International Workshop on Mining and Learning with Graphs. London, UK (2018)"},{"issue":"2","key":"48_CR9","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1109\/TKDE.2007.190689","volume":"20","author":"U Brandes","year":"2007","unstructured":"Brandes, U., et al.: On modularity clustering. IEEE Trans. Knowl. Data Eng. 20(2), 172\u2013188 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"48_CR10","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1007\/s10479-012-1286-z","volume":"222","author":"S Cafieri","year":"2014","unstructured":"Cafieri, S., Costa, A., Hansen, P.: Reformulation of a model for hierarchical divisive graph modularity maximization. Ann. Oper. Res. 222, 213\u2013226 (2014)","journal-title":"Ann. Oper. Res."},{"issue":"10","key":"48_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0205284","volume":"13","author":"S Chen","year":"2018","unstructured":"Chen, S., et al.: Global vs local modularity for network community detection. PLoS ONE 13(10), 1\u201321 (2018). https:\/\/doi.org\/10.1371\/journal.pone.0205284","journal-title":"PLoS ONE"},{"issue":"5","key":"48_CR12","doi-asserted-by":"publisher","DOI":"10.1088\/1742-5468\/aabfc8","volume":"2018","author":"T Chen","year":"2018","unstructured":"Chen, T., Singh, P., Bassler, K.E.: Network community detection using modularity density measures. J. Stat. Mech. Theory Exp. 2018(5), 053406 (2018). https:\/\/doi.org\/10.1088\/1742-5468\/aabfc8","journal-title":"J. Stat. Mech. Theory Exp."},{"issue":"6","key":"48_CR13","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.70.066111","volume":"70","author":"A Clauset","year":"2004","unstructured":"Clauset, A., Newman, M.E., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)","journal-title":"Phys. Rev. E"},{"key":"48_CR14","doi-asserted-by":"publisher","unstructured":"Dinh, T.N., Li, X., Thai, M.T.: Network clustering via maximizing modularity: approximation algorithms and theoretical limits. In: 2015 IEEE International Conference on Data Mining, pp. 101\u2013110 (2015). https:\/\/doi.org\/10.1109\/ICDM.2015.139","DOI":"10.1109\/ICDM.2015.139"},{"issue":"3","key":"48_CR15","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1080\/15427951.2014.950875","volume":"11","author":"TN Dinh","year":"2015","unstructured":"Dinh, T.N., Thai, M.T.: Toward optimal community detection: from trees to general weighted networks. Internet Math. 11(3), 181\u2013200 (2015)","journal-title":"Internet Math."},{"issue":"3\u20135","key":"48_CR16","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.physrep.2009.11.002","volume":"486","author":"S Fortunato","year":"2010","unstructured":"Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3\u20135), 75\u2013174 (2010)","journal-title":"Phys. Rep."},{"issue":"1","key":"48_CR17","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1073\/pnas.0605965104","volume":"104","author":"S Fortunato","year":"2007","unstructured":"Fortunato, S., Barth\u00e9lemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. 104(1), 36\u201341 (2007)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"48_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2016.09.002","volume":"659","author":"S Fortunato","year":"2016","unstructured":"Fortunato, S., Hric, D.: Community detection in networks: a user guide. Phys. Rep. 659, 1\u201344 (2016). https:\/\/doi.org\/10.1016\/j.physrep.2016.09.002","journal-title":"Phys. Rep."},{"key":"48_CR19","doi-asserted-by":"publisher","first-page":"848","DOI":"10.1038\/s41567-022-01716-7","volume":"18","author":"S Fortunato","year":"2022","unstructured":"Fortunato, S., Newman, M.E.: 20 years of network community detection. Nat. Phys. 18, 848\u2013850 (2022)","journal-title":"Nat. Phys."},{"issue":"4","key":"48_CR20","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.81.046106","volume":"81","author":"BH Good","year":"2010","unstructured":"Good, B.H., De Montjoye, Y.A., Clauset, A.: Performance of modularity maximization in practical contexts. Phys. Rev. E 81(4), 046106 (2010)","journal-title":"Phys. Rev. E"},{"key":"48_CR21","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.70.025101","volume":"70","author":"R Guimer\u00e0","year":"2004","unstructured":"Guimer\u00e0, R., Sales-Pardo, M., Amaral, L.A.N.: Modularity from fluctuations in random graphs and complex networks. Phys. Rev. E 70, 025101 (2004)","journal-title":"Phys. Rev. E"},{"key":"48_CR22","unstructured":"Gurobi Optimization Inc.: Gurobi optimizer reference manual (2023). https:\/\/gurobi.com\/documentation\/10.0\/refman\/index.html. Accessed 16 Feb 2023"},{"key":"48_CR23","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.83.016107","volume":"83","author":"B Karrer","year":"2011","unstructured":"Karrer, B., Newman, M.E.J.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83, 016107 (2011)","journal-title":"Phys. Rev. E"},{"issue":"1","key":"48_CR24","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.99.010301","volume":"99","author":"T Kawamoto","year":"2019","unstructured":"Kawamoto, T., Kabashima, Y.: Counting the number of metastable states in the modularity landscape: algorithmic detectability limit of greedy algorithms in community detection. Phys. Rev. E 99(1), 010301 (2019)","journal-title":"Phys. Rev. E"},{"key":"48_CR25","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.jcss.2020.11.005","volume":"117","author":"Y Kawase","year":"2021","unstructured":"Kawase, Y., Matsui, T., Miyauchi, A.: Additive approximation algorithms for modularity maximization. J. Comput. Syst. Sci. 117, 182\u2013201 (2021). https:\/\/doi.org\/10.1016\/j.jcss.2020.11.005","journal-title":"J. Comput. Syst. Sci."},{"issue":"2","key":"48_CR26","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1002\/j.1538-7305.1970.tb01770.x","volume":"49","author":"BW Kernighan","year":"1970","unstructured":"Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291\u2013307 (1970)","journal-title":"Bell Syst. Tech. J."},{"key":"48_CR27","volume-title":"The Stanford GraphBase: A Platform for Combinatorial Computing","author":"DE Knuth","year":"1993","unstructured":"Knuth, D.E.: The Stanford GraphBase: A Platform for Combinatorial Computing, vol. 1. ACM Press, New York (1993)"},{"key":"48_CR28","unstructured":"Kosowski, A., Saulpic, D., Mallmann-Trenn, F., Cohen-addad, V.P.: On the power of Louvain for graph clustering. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33 (NeurIPS\u201920) (2020)"},{"issue":"6","key":"48_CR29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.84.066122","volume":"84","author":"A Lancichinetti","year":"2011","unstructured":"Lancichinetti, A., Fortunato, S.: Limits of modularity maximization in community detection. Phys. Rev. E 84(6), 066122 (2011). https:\/\/doi.org\/10.1103\/PhysRevE.84.066122","journal-title":"Phys. Rev. E"},{"issue":"11","key":"48_CR30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.100.118703","volume":"100","author":"EA Leicht","year":"2008","unstructured":"Leicht, E.A., Newman, M.E.J.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008). https:\/\/doi.org\/10.1103\/PhysRevLett.100.118703","journal-title":"Phys. Rev. Lett."},{"key":"48_CR31","doi-asserted-by":"crossref","unstructured":"Li, P.Z., Huang, L., Wang, C.D., Lai, J.H.: EdMot: an edge enhancement approach for motif-aware community detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 479\u2013487 (2019)","DOI":"10.1145\/3292500.3330882"},{"issue":"3","key":"48_CR32","first-page":"1","volume":"15","author":"X Liu","year":"2021","unstructured":"Liu, X., et al.: A scalable redefined stochastic blockmodel. ACM Trans. Knowl. Discov. Data (TKDD) 15(3), 1\u201328 (2021)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"issue":"4","key":"48_CR33","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.96.042307","volume":"96","author":"BF Maier","year":"2017","unstructured":"Maier, B.F., Brockmann, D.: Cover time for random walks on arbitrary complex networks. Phys. Rev. E 96(4), 042307 (2017)","journal-title":"Phys. Rev. E"},{"issue":"1","key":"48_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42005-022-00890-7","volume":"5","author":"E Marchese","year":"2022","unstructured":"Marchese, E., Caldarelli, G., Squartini, T.: Detecting mesoscale structures by surprise. Commun. Phys. 5(1), 1\u201316 (2022)","journal-title":"Commun. Phys."},{"issue":"8","key":"48_CR35","doi-asserted-by":"publisher","first-page":"2174","DOI":"10.1007\/s00453-019-00649-7","volume":"82","author":"K Meeks","year":"2020","unstructured":"Meeks, K., Skerman, F.: The parameterised complexity of computing the maximum modularity of a graph. Algorithmica 82(8), 2174\u20132199 (2020)","journal-title":"Algorithmica"},{"key":"48_CR36","doi-asserted-by":"crossref","unstructured":"Miasnikof, P., Shestopaloff, A.Y., Bonner, A.J., Lawryshyn, Y., Pardalos, P.M.: A density-based statistical analysis of graph clustering algorithm performance. J. Complex Netw. 8(3), 1\u201333 (2020)","DOI":"10.1093\/comnet\/cnaa012"},{"issue":"23","key":"48_CR37","doi-asserted-by":"publisher","first-page":"8577","DOI":"10.1073\/pnas.0601602103","volume":"103","author":"MEJ Newman","year":"2006","unstructured":"Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577\u20138582 (2006). https:\/\/doi.org\/10.1073\/pnas.0601602103","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"5","key":"48_CR38","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.94.052315","volume":"94","author":"MEJ Newman","year":"2016","unstructured":"Newman, M.E.J.: Equivalence between modularity optimization and maximum likelihood methods for community detection. Phys. Rev. E 94(5), 052315 (2016). https:\/\/doi.org\/10.1103\/PhysRevE.94.052315","journal-title":"Phys. Rev. E"},{"issue":"5","key":"48_CR39","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.1602548","volume":"3","author":"L Peel","year":"2017","unstructured":"Peel, L., Larremore, D.B., Clauset, A.: The ground truth about metadata and community detection in networks. Sci. Adv. 3(5), e1602548 (2017)","journal-title":"Sci. Adv."},{"issue":"1","key":"48_CR40","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.89.012804","volume":"89","author":"TP Peixoto","year":"2014","unstructured":"Peixoto, T.P.: Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models. Phys. Rev. E 89(1), 012804 (2014)","journal-title":"Phys. Rev. E"},{"key":"48_CR41","doi-asserted-by":"crossref","unstructured":"Peixoto, T.P.: Descriptive vs. Inferential Community Detection in Networks: Pitfalls, Myths and Half-Truths. Elements in the Structure and Dynamics of Complex Networks, Cambridge University Press, Cambridge (2023)","DOI":"10.1017\/9781009118897"},{"issue":"1","key":"48_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0165-9","volume":"4","author":"G Rossetti","year":"2019","unstructured":"Rossetti, G., Milli, L., Cazabet, R.: CDLIB: a Python library to extract, compare and evaluate communities from complex networks. Appl. Netw. Sci. 4(1), 1\u201326 (2019)","journal-title":"Appl. Netw. Sci."},{"issue":"18","key":"48_CR43","doi-asserted-by":"publisher","first-page":"7327","DOI":"10.1073\/pnas.0611034104","volume":"104","author":"M Rosvall","year":"2007","unstructured":"Rosvall, M., Bergstrom, C.T.: An information-theoretic framework for resolving community structure in complex networks. Proc. Natl. Acad. Sci. 104(18), 7327\u20137331 (2007). https:\/\/doi.org\/10.1073\/pnas.0611034104","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"4","key":"48_CR44","doi-asserted-by":"publisher","first-page":"1118","DOI":"10.1073\/pnas.0706851105","volume":"105","author":"M Rosvall","year":"2008","unstructured":"Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105(4), 1118\u20131123 (2008). https:\/\/doi.org\/10.1073\/pnas.0706851105","journal-title":"Proc. Natl. Acad. Sci."},{"key":"48_CR45","unstructured":"Serrano, B., Vidal, T.: Community detection in the stochastic block model by mixed integer programming (2021)"},{"issue":"1","key":"48_CR46","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.90.012811","volume":"90","author":"S Sobolevsky","year":"2014","unstructured":"Sobolevsky, S., Campari, R., Belyi, A., Ratti, C.: General optimization technique for high-quality community detection in complex networks. Phys. Rev. E 90(1), 012811 (2014)","journal-title":"Phys. Rev. E"},{"key":"48_CR47","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.92.022816","volume":"92","author":"VA Traag","year":"2015","unstructured":"Traag, V.A., Aldecoa, R., Delvenne, J.C.: Detecting communities using asymptotical surprise. Phys. Rev. E 92, 022816 (2015). https:\/\/doi.org\/10.1103\/PhysRevE.92.022816","journal-title":"Phys. Rev. E"},{"key":"48_CR48","doi-asserted-by":"publisher","unstructured":"Traag, V.A., Waltman, L., van Eck, N.J.: From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9(1) (2019). https:\/\/doi.org\/10.1038\/s41598-019-41695-z","DOI":"10.1038\/s41598-019-41695-z"},{"key":"48_CR49","unstructured":"Vinh, N.X., Epps, J., Bailey, J.: Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance. J. Mach. Learn. Res. 11(95), 2837\u20132854 (2010). http:\/\/jmlr.org\/papers\/v11\/vinh10a.html"},{"issue":"51","key":"48_CR50","doi-asserted-by":"publisher","first-page":"18144","DOI":"10.1073\/pnas.1409770111","volume":"111","author":"P Zhang","year":"2014","unstructured":"Zhang, P., Moore, C.: Scalable detection of statistically significant communities and hierarchies, using message passing for modularity. Proc. Natl. Acad. Sci. 111(51), 18144\u201318149 (2014)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"48_CR51","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1016\/j.ins.2020.10.057","volume":"551","author":"X Zhao","year":"2021","unstructured":"Zhao, X., Liang, J., Wang, J.: A community detection algorithm based on graph compression for large-scale social networks. Inf. Sci. 551, 358\u2013372 (2021)","journal-title":"Inf. Sci."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36027-5_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,16]],"date-time":"2023-12-16T01:16:39Z","timestamp":1702689399000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36027-5_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031360268","9783031360275"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36027-5_48","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":"26 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Prague","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Czech Republic","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":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 July 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":"iccs-computsci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"530","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":"188","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":"94","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":"35% - 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":"2,8","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":"3,2","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)"}}]}}