{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:44:18Z","timestamp":1792784658654,"version":"4.5.0"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T00:00:00Z","timestamp":1634774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"GESIS \u2013 Leibniz-Institut f\u00fcr Sozialwissenschaften e.V.","id":[{"id":"https:\/\/ror.org\/018afyw53","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Netw Sci"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Social networks are very important carriers of information. For instance, the political leaning of our friends can serve as a proxy to identify our own political preferences. This explanatory power is leveraged in many scenarios ranging from business decision-making to scientific research to infer missing attributes using machine learning. However, factors affecting the performance and the direction of bias of these algorithms are not well understood. To this end, we systematically study how structural properties of the\n                    <jats:italic>network<\/jats:italic>\n                    and the\n                    <jats:italic>training sample<\/jats:italic>\n                    influence the results of collective classification. Our main findings show that (i) mean classification performance can empirically and analytically be predicted by structural properties such as homophily, class balance, edge density and sample size, (ii) small training samples are enough for heterophilic networks to achieve high and unbiased classification performance, even with imperfect model estimates, (iii) homophilic networks are more prone to bias issues and low performance when group size differences increase, (iv) when sampling budgets are small, partial crawls achieve the most accurate model estimates, and degree sampling achieves the highest overall performance. Our findings help practitioners to better understand and evaluate their results when sampling budgets are small or when no ground-truth is available.\n                  <\/jats:p>","DOI":"10.1007\/s41109-021-00394-3","type":"journal-article","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T05:02:41Z","timestamp":1634792561000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Explaining classification performance and bias via network structure and sampling technique"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3945-2966","authenticated-orcid":false,"given":"Lisette","family":"Esp\u00edn-Noboa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fariba","family":"Karimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruno","family":"Ribeiro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristina","family":"Lerman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,10,21]]},"reference":[{"issue":"4","key":"394_CR1","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1038\/s41562-018-0321-8","volume":"2","author":"KM Altenburger","year":"2018","unstructured":"Altenburger KM, Ugander J (2018) Monophily in social networks introduces similarity among friends-of-friends. Nat Hum Behav 2(4):284","journal-title":"Nat Hum Behav"},{"issue":"19","key":"394_CR2","doi-asserted-by":"publisher","first-page":"7310","DOI":"10.1126\/sciadv.aax7310","volume":"6","author":"A Asikainen","year":"2020","unstructured":"Asikainen A, I\u00f1iguez G, Ure\u00f1a-Carri\u00f3n J, Kaski K, Kivel\u00e4 M (2020) Cumulative effects of triadic closure and homophily in social networks. Sci Adv 6(19):7310","journal-title":"Sci Adv"},{"key":"394_CR3","doi-asserted-by":"crossref","unstructured":"Avrachenkov K, Ribeiro B, Sreedharan JK (2016) Inference in osns via lightweight partial crawls. In: Proceedings of the 2016 ACM SIGMETRICS international conference on measurement and modeling of computer science, ACM, pp 165\u2013177","DOI":"10.1145\/2896377.2901477"},{"issue":"2","key":"394_CR4","first-page":"1","volume":"16","author":"A Bachmann","year":"2011","unstructured":"Bachmann A, Becker A, Buerckner D, Hilker M, Kock F, Lehmann M, Tiburtius P, Funk B (2011) Online peer-to-peer lending-a literature review. J Internet Bank Commerce 16(2):1","journal-title":"J Internet Bank Commerce"},{"key":"394_CR5","doi-asserted-by":"crossref","unstructured":"Barab\u00e1si A-L (2009) Scale-free networks: a decade and beyond. Science 325(5939), 412\u2013413","DOI":"10.1126\/science.1173299"},{"key":"394_CR6","doi-asserted-by":"crossref","unstructured":"Binns R (2020) On the apparent conflict between individual and group fairness. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 514\u2013524 (2020)","DOI":"10.1145\/3351095.3372864"},{"issue":"1","key":"394_CR7","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.socnet.2005.05.001","volume":"28","author":"SP Borgatti","year":"2006","unstructured":"Borgatti SP, Carley K, Krackhardt D (2006) Robustness of centrality measures under conditions of imperfect data. Soc Netw 28(1):124\u2013136","journal-title":"Soc Netw"},{"key":"394_CR8","doi-asserted-by":"crossref","unstructured":"Brodersen KH, Ong CS, Stephan KE, Buhmann JM (2010) The balanced accuracy and its posterior distribution. In: 2010 20th international conference on pattern recognition, pp 3121\u20133124. IEEE","DOI":"10.1109\/ICPR.2010.764"},{"key":"394_CR9","doi-asserted-by":"crossref","unstructured":"Coscia M, Rossi L (2018) Benchmarking api costs of network sampling strategies. In: 2018 IEEE international conference on big data (Big Data), pp 663\u2013672. IEEE","DOI":"10.1109\/BigData.2018.8622486"},{"issue":"4","key":"394_CR10","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/s0378-8733(03)00012-1","volume":"25","author":"E Costenbader","year":"2003","unstructured":"Costenbader E, Valente TW (2003) The stability of centrality measures when networks are sampled. Soc Netw 25(4):283\u2013307. https:\/\/doi.org\/10.1016\/s0378-8733(03)00012-1","journal-title":"Soc Netw"},{"key":"394_CR11","doi-asserted-by":"crossref","unstructured":"Dixon L, Li J, Sorensen J, Thain N, Vasserman L (2018) Measuring and mitigating unintended bias in text classification. In: Proceedings of the 2018 AAAI\/ACM conference on AI, ethics, and society, pp 67\u201373","DOI":"10.1145\/3278721.3278729"},{"key":"394_CR12","doi-asserted-by":"crossref","unstructured":"Dong S, Liu D, Ouyang R, Zhu Y, Li L, Li T, Liu J (2019) Second-order markov assumption based bayes classifier for networked data with heterophily. IEEE Access","DOI":"10.1109\/ACCESS.2019.2892757"},{"key":"394_CR13","doi-asserted-by":"crossref","unstructured":"Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R (2012) Fairness through awareness. In: Proceedings of the 3rd innovations in theoretical computer science conference, pp 214\u2013226","DOI":"10.1145\/2090236.2090255"},{"key":"394_CR14","unstructured":"Dwork C, Immorlica N, Kalai AT, Leiserson M (2018) Decoupled classifiers for group-fair and efficient machine learning. In: conference on fairness, accountability and transparency, pp 119\u2013133 (2018)"},{"key":"394_CR15","unstructured":"Esp\u00edn-Noboa L (2021) Discrimination-in-relational-classification. GitHub Repository. https:\/\/github.com\/gesiscss\/Discrimination-in-Relational-Classification"},{"key":"394_CR16","doi-asserted-by":"crossref","unstructured":"Esp\u00edn-Noboa L, Wagner C, Karimi F, Lerman K (2018) Towards quantifying sampling bias in network inference. In: Companion of the the web conference 2018 on the web conference 2018, pp 1277\u20131285. International World Wide Web Conferences Steering Committee","DOI":"10.1145\/3184558.3191567"},{"key":"394_CR17","doi-asserted-by":"crossref","unstructured":"Farnadi G, Babaki B, Getoor L (2018) Fairness in relational domains. In: Proceedings of the 2018 AAAI\/ACM conference on AI, ethics, and society, pp 108\u2013114","DOI":"10.1145\/3278721.3278733"},{"issue":"4","key":"394_CR18","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1016\/0378-8733(91)90002-B","volume":"13","author":"J Galaskiewicz","year":"1991","unstructured":"Galaskiewicz J (1991) Estimating point centrality using different network sampling techniques. Soc Netw 13(4):347\u2013386","journal-title":"Soc Netw"},{"key":"394_CR19","doi-asserted-by":"crossref","unstructured":"Getoor L, Taskar B (2007) Introduction to statistical relational learning. MIT Press, Cambridge","DOI":"10.7551\/mitpress\/7432.001.0001"},{"key":"394_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.3389\/frai.2019.00003","volume":"2","author":"B Hadji Misheva","year":"2019","unstructured":"Hadji Misheva B, Spelta A, Giudici P (2019) Network based scoring models to improve credit risk management in peer to peer lending platforms. Front Artif Intell 2:3","journal-title":"Front Artif Intell"},{"key":"394_CR21","unstructured":"Hardt M, Price E, Srebro N (2016) Equality of opportunity in supervised learning. In: Advances in neural information processing systems, pp 3315\u20133323"},{"issue":"2","key":"394_CR22","doi-asserted-by":"publisher","first-page":"026107","DOI":"10.1103\/PhysRevE.65.026107","volume":"65","author":"P Holme","year":"2002","unstructured":"Holme P, Kim BJ (2002) Growing scale-free networks with tunable clustering. Phys Rev E 65(2):026107","journal-title":"Phys Rev E"},{"issue":"2","key":"394_CR23","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.chb.2011.11.001","volume":"28","author":"DJ Hughes","year":"2012","unstructured":"Hughes DJ, Rowe M, Batey M, Lee A (2012) A tale of two sites: Twitter vs. facebook and the personality predictors of social media usage. Comput Hum Behav 28(2):561\u2013569","journal-title":"Comput Hum Behav"},{"issue":"1","key":"394_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.21307\/joss-2019-051","volume":"10","author":"M Huisman","year":"2009","unstructured":"Huisman M (2009) Imputation of missing network data: some simple procedures. Soc Struct 10(1):1\u201329","journal-title":"Soc Struct"},{"key":"394_CR25","doi-asserted-by":"crossref","unstructured":"Jensen, D., Neville, J., Gallagher, B.: Why collective inference improves relational classification. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 593\u2013598 (2004). ACM","DOI":"10.1145\/1014052.1014125"},{"key":"394_CR26","doi-asserted-by":"crossref","unstructured":"Kallus N, Mao X, Zhou A (2019) Assessing algorithmic fairness with unobserved protected class using data combination. arXiv preprint arXiv:1906.00285","DOI":"10.1145\/3351095.3373154"},{"key":"394_CR27","doi-asserted-by":"crossref","unstructured":"Karimi F, G\u00e9nois M, Wagner C, Singer P, Strohmaier M (2018) Homophily influences ranking of minorities in social networks. Sci Rep 8","DOI":"10.1038\/s41598-018-29405-7"},{"key":"394_CR28","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1016\/j.socnet.2005.07.002","volume":"28","author":"G Kossinets","year":"2006","unstructured":"Kossinets G (2006) Effects of missing data in social networks. Soc Netw 28:247\u2013268","journal-title":"Soc Netw"},{"key":"394_CR29","doi-asserted-by":"crossref","unstructured":"Krasanakis, E., Spyromitros-Xioufis, E., Papadopoulos, S., Kompatsiaris, Y.: Adaptive sensitive reweighting to mitigate bias in fairness-aware classification. In: Proceedings of the 2018 world wide web conference, pp 853\u2013862 (2018)","DOI":"10.1145\/3178876.3186133"},{"key":"394_CR30","unstructured":"Kusner MJ, Loftus J, Russell C, Silva R (2017) Counterfactual fairness. In: Advances in neural information processing systems, pp 4066\u20134076"},{"issue":"1","key":"394_CR31","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1080\/00909882.2010.536844","volume":"39","author":"L Larrimore","year":"2011","unstructured":"Larrimore L, Jiang L, Larrimore J, Markowitz D, Gorski S (2011) Peer to peer lending: the relationship between language features, trustworthiness, and persuasion success. J Appl Commun Res 39(1):19\u201337","journal-title":"J Appl Commun Res"},{"key":"394_CR32","doi-asserted-by":"publisher","unstructured":"Lee J, Pfeffer J (2015) Estimating centrality statistics for complete and sampled networks: Some approaches and complications. In: 48th Hawaii international conference on system sciences, HICSS 2015, Kauai, Hawaii, USA, January 5\u20138, 2015, pp 1686\u20131695. https:\/\/doi.org\/10.1109\/HICSS.2015.203","DOI":"10.1109\/HICSS.2015.203"},{"key":"394_CR33","doi-asserted-by":"crossref","unstructured":"Leskovec J, Faloutsos C (2006) Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 631\u2013636. ACM","DOI":"10.1145\/1150402.1150479"},{"key":"394_CR34","doi-asserted-by":"crossref","unstructured":"Lin F, Cohen WW (2010) Semi-supervised classification of network data using very few labels. In: 2010 international conference on advances in social networks analysis and mining, pp 192\u2013199. IEEE","DOI":"10.1109\/ASONAM.2010.19"},{"issue":"1","key":"394_CR35","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1287\/mnsc.1120.1560","volume":"59","author":"M Lin","year":"2013","unstructured":"Lin M, Prabhala NR, Viswanathan S (2013) Judging borrowers by the company they keep: Friendship networks and information asymmetry in online peer-to-peer lending. Manage Sci 59(1):17\u201335","journal-title":"Manage Sci"},{"key":"394_CR36","doi-asserted-by":"crossref","unstructured":"Li J-Y, Yeh M-Y (2011) On sampling type distribution from heterogeneous social networks. In: Proceedings of the 15th pacific-asia conference on advances in knowledge discovery and data mining - volume Part II. PAKDD\u201911, pp 111\u2013122. Springer, Berlin, Heidelberg. http:\/\/dl.acm.org\/citation.cfm?id=2022850.2022860","DOI":"10.1007\/978-3-642-20847-8_10"},{"key":"394_CR37","doi-asserted-by":"publisher","unstructured":"Li Y, Ning Y, Liu R, Wu Y, Hui\u00a0Wang W (2020) Fairness of classification using users\u2019 social relationships in online peer-to-peer lending. In: Companion proceedings of the web conference 2020. WWW \u201920, pp. 733\u2013742. Association for Computing Machinery, New York, NY, USA. https:\/\/doi.org\/10.1145\/3366424.3383557","DOI":"10.1145\/3366424.3383557"},{"key":"394_CR38","first-page":"935","volume":"8","author":"SA Macskassy","year":"2007","unstructured":"Macskassy SA, Provost F (2007) Classification in networked data: a toolkit and a univariate case study. J Mach Learn Res 8:935\u2013983","journal-title":"J Mach Learn Res"},{"issue":"DC09","key":"394_CR39","first-page":"7","volume":"2009","author":"LB Marinho","year":"2009","unstructured":"Marinho LB, Preisach C, Schmidt-Thieme L et al (2009) Relational classification for personalized tag recommendation. ECML PKDD Discov Chall 2009(DC09):7","journal-title":"ECML PKDD Discov Chall"},{"issue":"1","key":"394_CR40","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1146\/annurev.soc.27.1.415","volume":"27","author":"M McPherson","year":"2001","unstructured":"McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Ann Rev Sociol 27(1):415\u2013444. https:\/\/doi.org\/10.1146\/annurev.soc.27.1.415","journal-title":"Ann Rev Sociol"},{"key":"394_CR41","unstructured":"Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A (2019) A survey on bias and fairness in machine learning. arXiv preprint arXiv:1908.09635"},{"key":"394_CR42","unstructured":"Neville J, Jensen D (2000) Iterative classification in relational data. In: Proceedings of the AAAI-2000 workshop on learning statistical models from relational data, pp 13\u201320"},{"issue":"2","key":"394_CR43","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1073\/pnas.98.2.404","volume":"98","author":"ME Newman","year":"2001","unstructured":"Newman ME (2001) The structure of scientific collaboration networks. Proc Natl Acad Sci 98(2):404\u2013409","journal-title":"Proc Natl Acad Sci"},{"key":"394_CR44","doi-asserted-by":"crossref","unstructured":"Peel L (2017) Graph-based semi-supervised learning for relational networks. In: Proceedings of the 2017 SIAM international conference on data mining, pp 435\u2013443 (2017). SIAM. http:\/\/hdl.handle.net\/2078.1\/182929","DOI":"10.1137\/1.9781611974973.49"},{"issue":"16","key":"394_CR45","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1073\/pnas.1713019115","volume":"115","author":"L Peel","year":"2018","unstructured":"Peel L, Delvenne J-C, Lambiotte R (2018) Multiscale mixing patterns in networks. Proc Natl Acad Sci 115(16):4057\u20134062","journal-title":"Proc Natl Acad Sci"},{"key":"394_CR46","doi-asserted-by":"crossref","unstructured":"Raghavan M, Barocas S, Kleinberg J, Levy K (2020) Mitigating bias in algorithmic hiring: evaluating claims and practices. In: Proceedings of the 2020 conference on fairness, accountability, and transparency, pp 469\u2013481","DOI":"10.1145\/3351095.3372828"},{"issue":"13","key":"394_CR47","doi-asserted-by":"publisher","first-page":"5706","DOI":"10.1073\/pnas.0914080107","volume":"107","author":"LEC Rocha","year":"2010","unstructured":"Rocha LEC, Liljeros F, Holme P (2010) Information dynamics shape the sexual networks of Internet-mediated prostitution. Proc Natl Acad Sci 107(13):5706\u20135711","journal-title":"Proc Natl Acad Sci"},{"key":"394_CR48","unstructured":"Rozemberczki B, Allen C, Sarkar R (2019) Multi-scale attributed node embedding (2019). arXiv:1909.13021"},{"issue":"3","key":"394_CR49","first-page":"93","volume":"29","author":"P Sen","year":"2008","unstructured":"Sen P, Namata G, Bilgic M, Getoor L, Galligher B, Eliassi-Rad T (2008) Collective classification in network data. AI Mag 29(3):93\u2013106","journal-title":"AI Mag"},{"issue":"16","key":"394_CR50","doi-asserted-by":"publisher","first-page":"4165","DOI":"10.1016\/j.physa.2011.12.021","volume":"391","author":"AL Traud","year":"2012","unstructured":"Traud AL, Mucha PJ, Porter MA (2012) Social structure of facebook networks. Physica A 391(16):4165\u20134180","journal-title":"Physica A"},{"key":"394_CR51","doi-asserted-by":"crossref","unstructured":"Verma S, Rubin J (2018) Fairness definitions explained. In: 2018 IEEE\/ACM international workshop on software fairness (FairWare), pp 1\u20137. IEEE","DOI":"10.1145\/3194770.3194776"},{"key":"394_CR52","doi-asserted-by":"publisher","unstructured":"Wagner C Politicians on Wikipedia and DBpedia (Version: 1.0.0) (2017) https:\/\/doi.org\/10.7802\/1515. GESIS - Leibniz-Institute for the Social Sciences","DOI":"10.7802\/1515"},{"key":"394_CR53","doi-asserted-by":"publisher","unstructured":"Wagner C, Graells-Garrido E, Garcia D, Menczer F (2016) Women through the glass ceiling: gender asymmetries in wikipedia. EPJ Data Sci. 5(5). https:\/\/doi.org\/10.1140\/epjds\/s13688-016-0066-4","DOI":"10.1140\/epjds\/s13688-016-0066-4"},{"issue":"4","key":"394_CR54","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.socnet.2012.01.003","volume":"34","author":"DJ Wang","year":"2012","unstructured":"Wang DJ, Shi X, McFarland DA, Leskovec J (2012) Measurement error in network data: a re-classification. Soc Netw 34(4):396\u2013409","journal-title":"Soc Netw"},{"key":"394_CR55","unstructured":"Yang J, Ribeiro B, Neville J (2017) Should we be confident in peer effects estimated from social network crawls? In: Proceedings of the Eleventh international conference on web and social media, ICWSM 2017, Montr\u00e9al, Qu\u00e9bec, Canada, May 15\u201318, 2017, pp 708\u2013711. https:\/\/aaai.org\/ocs\/index.php\/ICWSM\/ICWSM17\/paper\/view\/15696"},{"key":"394_CR56","doi-asserted-by":"crossref","unstructured":"Zafar MB, Valera I, Gomez\u00a0Rodriguez M, Gummadi KP (2017) Fairness beyond disparate treatment and disparate impact: learning classification without disparate mistreatment. In: Proceedings of the 26th international conference on world wide web, pp 1171\u20131180 (2017)","DOI":"10.1145\/3038912.3052660"},{"key":"394_CR57","unstructured":"Zeno G, Neville J (2016) Investigating the impact of graph structure and attribute correlation on collective classification performance"},{"key":"394_CR58","unstructured":"Zhang Y, Ramesh A (2020) Learning fairness-aware relational structures. ECAI (2020). arXiv preprint arXiv:2002.09471"},{"key":"394_CR59","doi-asserted-by":"crossref","unstructured":"Zheleva E, Getoor L (2009) To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In: Proceedings of the 18th international conference on world wide web, pp 531\u2013540","DOI":"10.1145\/1526709.1526781"}],"container-title":["Applied Network Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41109-021-00394-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s41109-021-00394-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s41109-021-00394-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T05:33:53Z","timestamp":1634794433000},"score":1,"resource":{"primary":{"URL":"https:\/\/appliednetsci.springeropen.com\/articles\/10.1007\/s41109-021-00394-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,21]]},"references-count":59,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["394"],"URL":"https:\/\/doi.org\/10.1007\/s41109-021-00394-3","relation":{},"ISSN":["2364-8228"],"issn-type":[{"value":"2364-8228","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,21]]},"assertion":[{"value":"13 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"78"}}