{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T22:32:46Z","timestamp":1766269966330,"version":"3.37.3"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T00:00:00Z","timestamp":1571270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T00:00:00Z","timestamp":1571270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"ARC","award":["14\/19-060"],"award-info":[{"award-number":["14\/19-060"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Netw Sci"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n              <jats:p>A main challenge in mining network-based data is finding effective ways to represent or encode graph structures so that it can be efficiently exploited by machine learning algorithms. Several methods have focused in network representation at node\/edge or substructure level. However, many real life challenges related with time-varying, multilayer, chemical compounds and brain networks involve analysis of a family of graphs instead of single one opening additional challenges in graph comparison and representation. Traditional approaches for learning representations relies on hand-crafted specialized features to extract meaningful information about the graphs, e.g. statistical properties, structural motifs, etc. as well as popular graph distances to quantify dissimilarity between networks. In this work we provide an unsupervised approach to learn graph embeddings for a collection of graphs defined on the same set of nodes so that it can be used in numerous graph mining tasks. By using an unsupervised neural network approach on input graphs, we aim to capture the underlying distribution of the data in order to discriminate between different class of networks. Our method is assessed empirically on synthetic and real life datasets and evaluated in three different tasks: graph clustering, visualization and classification. Results reveal that our method outperforms well known graph distances and graph-kernels in clustering and classification tasks, being highly efficient in runtime.<\/jats:p>","DOI":"10.1007\/s41109-019-0197-1","type":"journal-article","created":{"date-parts":[[2019,10,17]],"date-time":"2019-10-17T15:01:19Z","timestamp":1571324479000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Unsupervised network embeddings with node identity awareness"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6405-3775","authenticated-orcid":false,"given":"Leonardo","family":"Guti\u00e9rrez-G\u00f3mez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Charles","family":"Delvenne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,17]]},"reference":[{"key":"197_CR1","doi-asserted-by":"crossref","unstructured":"Masuda, N, Holme P (2019) Detecting sequences of system states in temporal networks. Sci Rep 9(1).","DOI":"10.1038\/s41598-018-37534-2"},{"issue":"8","key":"197_CR2","doi-asserted-by":"publisher","first-page":"23176","DOI":"10.1371\/journal.pone.0023176","volume":"6","author":"J Stehl\u00e9","year":"2011","unstructured":"Stehl\u00e9, J, Voirin N, Barrat A, Cattuto C, Isella L, Pinton J, Quaggiotto M, Van den Broeck W, R\u00e9gis C, Lina B, Vanhems P (2011) High-resolution measurements of face-to-face contact patterns in a primary school. PLOS ONE 6(8):23176. \n                    https:\/\/doi.org\/10.1371\/journal.pone.0023176\n                    \n                  .","journal-title":"PLOS ONE"},{"issue":"7","key":"197_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pbio.0060159","volume":"6","author":"P Hagmann","year":"2008","unstructured":"Hagmann, P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O (2008) Mapping the structural core of human cerebral cortex. PLOS Biol 6(7):1\u201315. \n                    https:\/\/doi.org\/10.1371\/journal.pbio.0060159\n                    \n                  .","journal-title":"PLOS Biol"},{"key":"197_CR4","doi-asserted-by":"publisher","first-page":"258","DOI":"10.1007\/978-3-642-14366-3_23","volume-title":"Biomedical Image Registration","author":"ND Cahill","year":"2010","unstructured":"Cahill, ND (2010) Normalized measures of mutual information with general definitions of entropy for multimodal image registration. In: Fischer B, Dawant BM, Lorenz C (eds)Biomedical Image Registration, 258\u2013268.. Springer, Berlin, Heidelberg."},{"issue":"2","key":"197_CR5","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1214\/18-AOAS1176","volume":"12","author":"C Donnat","year":"2018","unstructured":"Donnat, C, Holmes S (2018) Tracking network dynamics: A survey using graph distances. Ann Appl Stat 12(2):971\u20131012. \n                    https:\/\/doi.org\/10.1214\/18-AOAS1176\n                    \n                  . \n                    https:\/\/projecteuclid.org\/euclid.aoas\/1532743483\n                    \n                  .","journal-title":"Ann Appl Stat"},{"key":"197_CR6","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B, Smola A (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge. Max-Planck-Gesellschaft."},{"key":"197_CR7","unstructured":"Barnett, I, Malik N, Kuijjer ML, Mucha PJ, Onnela J. -P. (2016) Feature-based classification of networks. http:\/\/arxiv.org\/abs\/1610.05868."},{"key":"197_CR8","doi-asserted-by":"crossref","unstructured":"Chi\u00eam, B, Crevecoeur JCDF (2018) Supervised Classification of Structural Brain Networks Reveals Gender Differences In: 2018 19th IEEE Mediterranean Electrotechnical Conference (MELECON).","DOI":"10.1109\/MELCON.2018.8379106"},{"key":"197_CR9","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1145\/130385.130401","volume-title":"Proceedings of the Fifth Annual Workshop on Computational Learning Theory. COLT \u201992","author":"BE Boser","year":"1992","unstructured":"Boser, BE, Guyon IM, Vapnik VN (1992) A training algorithm for optimal margin classifiers In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. COLT \u201992, 144\u2013152.. ACM, New York, NY, USA. \n                    https:\/\/doi.org\/10.1145\/130385.130401\n                    \n                  . \n                    http:\/\/doi.acm.org\/10.1145\/130385.130401\n                    \n                  ."},{"key":"197_CR10","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.1038\/srep01344","volume":"3","author":"A Cardillo","year":"2013","unstructured":"Cardillo, A, G\u00f3mez-Garde\u00f1es J, Zanin M, Romance M, Papo D, del Pozo F, Boccaletti S (2013) Emergence of network features from multiplexity. Sci Rep 3:1344. http:\/\/arxiv.org\/abs\/1212.2153. \n                    https:\/\/doi.org\/10.1038\/srep01344\n                    \n                  .","journal-title":"Sci Rep"},{"key":"197_CR11","unstructured":"Ma, G, Ahmed NK, Willke TL, Sengupta D, Cole MW, Turk-Browne NB, Yu PS (2018) Similarity learning with higher-order proximity for brain network analysis. CoRR abs\/1811.02662. http:\/\/arxiv.org\/abs\/1811.02662."},{"issue":"9","key":"197_CR12","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","volume":"30","author":"H Cai","year":"2017","unstructured":"Cai, H, Zheng VW, Chang KC (2017) A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications. IEEE Trans Knowl Data Eng 30(9):1616\u20131637. \n                    https:\/\/doi.org\/10.1109\/TKDE.2018.2807452\n                    \n                  .","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"197_CR13","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.knosys.2018.03.022","volume":"151","author":"P Goyal","year":"2018","unstructured":"Goyal, P, Ferrara E (2018) Graph embedding techniques, applications, and performance: A survey. Knowledge-Based Syst 151:78\u201394. \n                    https:\/\/doi.org\/10.1016\/j.knosys.2018.03.022\n                    \n                  .","journal-title":"Knowledge-Based Syst"},{"key":"197_CR14","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.neucom.2018.01.007","volume":"284","author":"H Choi","year":"2018","unstructured":"Choi, H, Cho K, Bengio Y (2018) Fine-grained attention mechanism for neural machine translation. Neurocomputing 284:171\u2013176. \n                    https:\/\/doi.org\/10.1016\/j.neucom.2018.01.007\n                    \n                  .","journal-title":"Neurocomputing"},{"issue":"2","key":"197_CR15","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1021\/jm00106a046","volume":"34","author":"AK Debnath","year":"1991","unstructured":"Debnath, AK, Lopez de Compadre RL, Debnath G, Shusterman AJ, Hansch C (1991) Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity. J Med Chem 34(2):786\u2013797. \n                    https:\/\/doi.org\/10.1021\/jm00106a046\n                    \n                  . \n                    https:\/\/doi.org\/10.1021\/jm00106a046\n                    \n                  .","journal-title":"J Med Chem"},{"issue":"1","key":"197_CR16","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1002\/hbm.22633","volume":"36","author":"A Griffa","year":"2015","unstructured":"Griffa, A, Baumann PS, Ferrari C, Do KQ, Conus P, Thiran J. -P., Hagmann P (2015) Characterizing the connectome in schizophrenia with diffusion spectrum imaging. Human Brain Mapping 36(1):354\u2013366. \n                    https:\/\/doi.org\/10.1002\/hbm.22633\n                    \n                  . \n                    https:\/\/doi.org\/10.1002\/hbm.22633\n                    \n                  .","journal-title":"Human Brain Mapping"},{"issue":"4","key":"197_CR17","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1093\/comnet\/cny034","volume":"7","author":"L Guti\u00e9rrez-G\u00f3mez","year":"2019","unstructured":"Guti\u00e9rrez-G\u00f3mez, L, Delvenne J. -C. (2019) Multi-hop assortativities for network classification. J Compl Netw 7(4):603\u2013622. \n                    https:\/\/doi.org\/10.1093\/comnet\/cny034\n                    \n                  .","journal-title":"J Compl Netw"},{"issue":"2","key":"197_CR18","doi-asserted-by":"publisher","first-page":"167","DOI":"10.1137\/S003614450342480","volume":"45","author":"M Newman","year":"2003","unstructured":"Newman, M (2003) The structure and function of complex networks. SIAM Rev 45(2):167\u2013256. \n                    https:\/\/doi.org\/10.1137\/S003614450342480\n                    \n                  .","journal-title":"SIAM Rev"},{"key":"197_CR19","first-page":"27","volume-title":"Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools. VALUETOOLS \u201909","author":"S Fortunato","year":"2009","unstructured":"Fortunato, S, Lancichinetti A (2009) Community detection algorithms: A comparative analysis: Invited presentation, extended abstract In: Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools. VALUETOOLS \u201909, 27\u20131272.. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels. \n                    http:\/\/dl.acm.org\/citation.cfm?id=1698822.1698858\n                    \n                  ."},{"issue":"3","key":"197_CR20","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1145\/2824443","volume":"10","author":"D Koutra","year":"2016","unstructured":"Koutra, D, Shah N, Vogelstein JT, Gallagher B, Faloutsos C (2016) Deltacon: Principled massive-graph similarity function with attribution. ACM Trans Knowl Discov Data 10(3):28\u201312843. \n                    https:\/\/doi.org\/10.1145\/2824443\n                    \n                  .","journal-title":"ACM Trans Knowl Discov Data"},{"issue":"3","key":"197_CR21","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/s10044-012-0284-8","volume":"16","author":"L Livi","year":"2013","unstructured":"Livi, L, Rizzi A (2013) The graph matching problem. Pattern Anal Appl 16(3):253\u2013283. \n                    https:\/\/doi.org\/10.1007\/s10044-012-0284-8\n                    \n                  .","journal-title":"Pattern Anal Appl"},{"issue":"11","key":"197_CR22","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y. Lecun","year":"1998","unstructured":"Lecun, Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition In: Proceedings of the IEEE, 2278\u20132324.","journal-title":"Proceedings of the IEEE"},{"key":"197_CR23","unstructured":"Le, Q, Mikolov T (2014) Distributed representations of sentences and documents In: Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32. ICML\u201914, 1188\u20131196.. JMLR.org. \n                    http:\/\/dl.acm.org\/citation.cfm?id=3044805.3045025\n                    \n                  ."},{"key":"197_CR24","doi-asserted-by":"publisher","first-page":"1666","DOI":"10.1145\/3219819.3219980","volume-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD \u201918","author":"JB Lee","year":"2018","unstructured":"Lee, JB, Rossi R, Kong X (2018) Graph classification using structural attention In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD \u201918, 1666\u20131674.. ACM, New York. \n                    https:\/\/doi.org\/10.1145\/3219819.3219980\n                    \n                  . \n                    http:\/\/doi.acm.org\/10.1145\/3219819.3219980\n                    \n                  ."},{"issue":"Complete","key":"197_CR25","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.neucom.2014.12.095","volume":"169","author":"JA Lee","year":"2015","unstructured":"Lee, JA, Peluffo-Ord\u00f3\u00f1ez DH, Verleysen M (2015) Multi-scale similarities in stochastic neighbour embedding: Reducing dimensionality while preserving both local and global structure. Neurocomputing 169(Complete):246\u2013261. \n                    https:\/\/doi.org\/10.1016\/j.neucom.2014.12.095\n                    \n                  .","journal-title":"Neurocomputing"},{"key":"197_CR26","first-page":"3111","volume-title":"Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201913","author":"T Mikolov","year":"2013","unstructured":"Mikolov, T, Sutskever I, Chen K, Corrado G, Dean J (2013) Distributed representations of words and phrases and their compositionality In: Proceedings of the 26th International Conference on Neural Information Processing Systems - Volume 2. NIPS\u201913, 3111\u20133119.. Curran Associates Inc, USA. \n                    http:\/\/dl.acm.org\/citation.cfm?id=2999792.2999959\n                    \n                  ."},{"key":"197_CR27","unstructured":"Narayanan, A, Chandramohan M, Chen L, Liu Y, Saminathan S (2016) subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs In: MLGWorkshop.. KDD\u201916 Workshop."},{"key":"197_CR28","unstructured":"Narayanan, A, Chandramohan M, Venkatesan R, Chen L, Liu Y (2017) graph2vec: Learning distributed representations of graphs In: 15th International Workshop on Mining and Learning with Graphs.. MLGWorkshop 2017."},{"key":"197_CR29","unstructured":"Niepert, M, Ahmed M, Kutzkov K (2016) Learning convolutional neural networks for graphs In: Proceedings of The 33rd International Conference on Machine Learning, vol. 48, 2014\u20132023.. PMLR. http:\/\/arxiv.org\/abs\/1605.05273."},{"key":"197_CR30","unstructured":"Ng, AY, Jordan MI, Weiss Y (2002) On spectral clustering: Analysis and an algorithm. In: Dietterich TG, Becker S, Ghahramani Z (eds)Advances in Neural Information Processing Systems 14, 849\u2013856.. MIT Press. \n                    http:\/\/papers.nips.cc\/paper\/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf\n                    \n                  ."},{"key":"197_CR31","first-page":"2914","volume-title":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI\u201915","author":"L Peel","year":"2015","unstructured":"Peel, L, Clauset A (2015) Detecting change points in the large-scale structure of evolving networks In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence. AAAI\u201915, 2914\u20132920.. AAAI Press, Austin. \n                    http:\/\/dl.acm.org\/citation.cfm?id=2888116.2888122\n                    \n                  ."},{"issue":"9","key":"197_CR32","doi-asserted-by":"publisher","first-page":"2658","DOI":"10.1073\/pnas.0400054101","volume":"101","author":"F Radicchi","year":"2004","unstructured":"Radicchi, F, Castellano C, Cecconi F, Loreto V, Parisi D (2004) Defining and identifying communities in networks. Proc Nat Acad Sci 101(9):2658\u20132663. \n                    https:\/\/doi.org\/10.1073\/pnas.0400054101\n                    \n                  . \n                    https:\/\/doi.org\/10.1073\/pnas.0400054101\n                    \n                  .","journal-title":"Proc Nat Acad Sci"},{"key":"197_CR33","first-page":"2539","volume":"12","author":"N Shervashidze","year":"2011","unstructured":"Shervashidze, N, Schweitzer P, van Leeuwen EJ, Mehlhorn K, Borgwardt KM (2011) Weisfeiler-lehman graph kernels. J Mach Learn Res 12:2539\u20132561.","journal-title":"J Mach Learn Res"},{"key":"197_CR34","first-page":"4","volume-title":"Proceedings of the 15th International Joint Conference on Artifical Intelligence - Volume 1, IJCAI\u201997","author":"A Srinivasan","year":"1997","unstructured":"Srinivasan, A, King RD, Muggleton SH, Sternberg MJE (1997) The predictive toxicology evaluation challenge In: Proceedings of the 15th International Joint Conference on Artifical Intelligence - Volume 1, IJCAI\u201997, 4\u20139.. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA. \n                    http:\/\/dl.acm.org\/citation.cfm?id=1624162.1624163\n                    \n                  ."},{"key":"197_CR35","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1145\/1390156.1390294","volume-title":"Proceedings of the 25th International Conference on Machine Learning. ICML \u201908","author":"P Vincent","year":"2008","unstructured":"Vincent, P, Larochelle H, Bengio Y, Manzagol P-A (2008) Extracting and composing robust features with denoising autoencoders In: Proceedings of the 25th International Conference on Machine Learning. ICML \u201908, 1096\u20131103.. ACM, New York. \n                    https:\/\/doi.org\/10.1145\/1390156.1390294\n                    \n                  . \n                    http:\/\/doi.acm.org\/10.1145\/1390156.1390294\n                    \n                  ."},{"key":"197_CR36","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371\u20133408.","journal-title":"J Mach Learn Res"},{"issue":"9","key":"197_CR37","doi-asserted-by":"publisher","first-page":"2833","DOI":"10.1016\/j.patcog.2008.03.011","volume":"41","author":"RC Wilson","year":"2008","unstructured":"Wilson, RC, Zhu P (2008) A study of graph spectra for comparing graphs and trees. Pattern Recogn 41(9):2833\u20132841. \n                    https:\/\/doi.org\/10.1016\/j.patcog.2008.03.011\n                    \n                  .","journal-title":"Pattern Recogn"},{"key":"197_CR38","unstructured":"Wu, G, Chang EY, Zhang Z (2005) An analysis of transformation on non-positive semidefinite similarity matrix for kernel machines In: Proceedings of the 22nd International Conference on Machine Learning.. International Conference on Machine Learning (ICML)."},{"key":"197_CR39","unstructured":"Xu, K, Wu L, Wang Z, Feng Y, Witbrock M, Sheinin V (2018) Graph2seq: Graph to sequence learning with attention-based neural networks In: arXiv Preprint arXiv:1804.00823."},{"key":"197_CR40","doi-asserted-by":"publisher","first-page":"1365","DOI":"10.1145\/2783258.2783417","volume-title":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201915","author":"P Yanardag","year":"2015","unstructured":"Yanardag, P, Vishwanathan SVN (2015) Deep graph kernels In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD \u201915, 1365\u20131374.. ACM, New York. \n                    https:\/\/doi.org\/10.1145\/2783258.2783417\n                    \n                  . \n                    http:\/\/doi.acm.org\/10.1145\/2783258.2783417\n                    \n                  ."}],"container-title":["Applied Network Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41109-019-0197-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41109-019-0197-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41109-019-0197-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,15]],"date-time":"2020-10-15T23:28:43Z","timestamp":1602804523000},"score":1,"resource":{"primary":{"URL":"https:\/\/appliednetsci.springeropen.com\/articles\/10.1007\/s41109-019-0197-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,17]]},"references-count":40,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["197"],"URL":"https:\/\/doi.org\/10.1007\/s41109-019-0197-1","relation":{},"ISSN":["2364-8228"],"issn-type":[{"type":"electronic","value":"2364-8228"}],"subject":[],"published":{"date-parts":[[2019,10,17]]},"assertion":[{"value":"1 March 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 August 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 October 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare that they have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"82"}}