{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T15:51:47Z","timestamp":1776095507314,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Consiglio Nazionale Delle Ricerche"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2024,10]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Groups\u2014such as clusters of points or communities of nodes\u2014are fundamental when addressing various data mining tasks. In temporal data, the predominant approach for characterizing group evolution has been through the identification of \u201cevents\u201d. However, the events usually described in the literature, e.g., shrinks\/growths, splits\/merges, are often arbitrarily defined, creating a gap between such theoretical\/predefined types and real-data group observations. Moving beyond existing taxonomies, we think of events as \u201carchetypes\u201d characterized by a unique combination of quantitative dimensions that we call \u201cfacets\u201d. Group dynamics are defined by their position within the facet space, where archetypal events occupy extremities. Thus, rather than enforcing strict event types, our approach can allow for hybrid descriptions of dynamics involving group proximity to multiple archetypes. We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics with respect to state-of-the-art methods, especially when the groups are subject to complex relationships. Our approach also offers intuitive solutions to common tasks related to dynamic group analysis, such as choosing an appropriate aggregation scale, quantifying partition stability, and evaluating event quality.<\/jats:p>","DOI":"10.1007\/s10994-024-06600-4","type":"journal-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T16:02:14Z","timestamp":1722528134000},"page":"7591-7615","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Describing group evolution in temporal data using multi-faceted events"],"prefix":"10.1007","volume":"113","author":[{"given":"Andrea","family":"Failla","sequence":"first","affiliation":[]},{"given":"R\u00e9my","family":"Cazabet","sequence":"additional","affiliation":[]},{"given":"Giulio","family":"Rossetti","sequence":"additional","affiliation":[]},{"given":"Salvatore","family":"Citraro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,1]]},"reference":[{"key":"6600_CR1","doi-asserted-by":"publisher","first-page":"2381","DOI":"10.1007\/s10462-019-09736-1","volume":"53","author":"MY Ansari","year":"2020","unstructured":"Ansari, M. Y., Ahmad, A., Khan, S. S., & Bhushan, G. (2020). Mainuddin: Spatiotemporal clustering\u2014A review. Artificial Intelligence Review, 53, 2381\u20132423.","journal-title":"Artificial Intelligence Review"},{"issue":"4","key":"6600_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1631162.1631164","volume":"3","author":"S Asur","year":"2009","unstructured":"Asur, S., Parthasarathy, S., & Ucar, D. (2009). An event-based framework for characterizing the evolutionary behavior of interaction graphs. ACM Transactions on Knowledge Discovery from Data (TKDD), 3(4), 1\u201336.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"issue":"10","key":"6600_CR3","doi-asserted-by":"publisher","first-page":"10008","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. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), 10008.","journal-title":"Journal of Statistical Mechanics: Theory and Experiment"},{"issue":"19","key":"6600_CR4","doi-asserted-by":"publisher","first-page":"3063","DOI":"10.1126\/sciadv.abj3063","volume":"8","author":"A Bovet","year":"2022","unstructured":"Bovet, A., Delvenne, J.-C., & Lambiotte, R. (2022). Flow stability for dynamic community detection. Science Advances, 8(19), 3063.","journal-title":"Science Advances"},{"key":"6600_CR5","doi-asserted-by":"crossref","unstructured":"Brodka, P., Musial, K., & Kazienko, P. (2009). A performance of centrality calculation in social networks. In 2009 international conference on computational aspects of social networks (pp. 24\u201331). IEEE.","DOI":"10.1109\/CASoN.2009.20"},{"key":"6600_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-012-0058-8","volume":"3","author":"P Br\u00f3dka","year":"2013","unstructured":"Br\u00f3dka, P., Saganowski, S., & Kazienko, P. (2013). GED: The method for group evolution discovery in social networks. Social Network Analysis and Mining, 3, 1\u201314.","journal-title":"Social Network Analysis and Mining"},{"key":"6600_CR7","doi-asserted-by":"crossref","unstructured":"Cazabet, R. (2021). Data compression to choose a proper dynamic network representation. In Complex networks & their applications IX: Volume 1, Proceedings of the ninth international conference on complex networks and their applications COMPLEX NETWORKS 2020 (pp. 522\u2013532). Springer.","DOI":"10.1007\/978-3-030-65347-7_43"},{"key":"6600_CR8","doi-asserted-by":"publisher","unstructured":"Cazabet, R., Rossetti, G., & Amblard, F. (2018). In: Alhajj, R., Rokne, J. (Eds.), Dynamic community detection (pp. 669\u2013678). Springer. https:\/\/doi.org\/10.1007\/978-1-4939-7131-2_383","DOI":"10.1007\/978-1-4939-7131-2_383"},{"issue":"6","key":"6600_CR9","first-page":"027","volume":"8","author":"R Cazabet","year":"2020","unstructured":"Cazabet, R., Boudebza, S., & Rossetti, G. (2020). Evaluating community detection algorithms for progressively evolving graphs. Journal of Complex Networks, 8(6), 027.","journal-title":"Journal of Complex Networks"},{"issue":"1","key":"6600_CR10","doi-asserted-by":"publisher","first-page":"39713","DOI":"10.1038\/srep39713","volume":"6","author":"RK Darst","year":"2016","unstructured":"Darst, R. K., Granell, C., Arenas, A., G\u00f3mez, S., Saram\u00e4ki, J., & Fortunato, S. (2016). Detection of timescales in evolving complex systems. Scientific Reports, 6(1), 39713.","journal-title":"Scientific Reports"},{"key":"6600_CR11","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. (2016). Community detection in networks: A user guide. Physics Reports, 659, 1\u201344.","journal-title":"Physics Reports"},{"key":"6600_CR12","doi-asserted-by":"crossref","unstructured":"Gliwa, B., Saganowski, S., Zygmunt, A., Br\u00f3dka, P., Kazienko, P., & Kozak, J. (2012). Identification of group changes in blogosphere. In 2012 IEEE\/ACM international conference on advances in social networks analysis and mining (pp. 1201\u20131206). IEEE.","DOI":"10.1109\/ASONAM.2012.207"},{"key":"6600_CR13","doi-asserted-by":"crossref","unstructured":"Greene, D., Doyle, D., & Cunningham, P. (2010). Tracking the evolution of communities in dynamic social networks. In 2010 international conference on advances in social networks analysis and mining (pp. 176\u2013183). IEEE.","DOI":"10.1109\/ASONAM.2010.17"},{"key":"6600_CR14","doi-asserted-by":"publisher","first-page":"5249","DOI":"10.1073\/pnas.0307750100","volume":"101","author":"J Hopcroft","year":"2004","unstructured":"Hopcroft, J., Khan, O., Kulis, B., & Selman, B. (2004). Tracking evolving communities in large linked networks. Proceedings of the National Academy of Sciences, 101, 5249\u20135253.","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"6600_CR15","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of classification, 2, 193\u2013218.","journal-title":"Journal of classification"},{"key":"6600_CR16","doi-asserted-by":"crossref","unstructured":"\u0130lhan, N., & \u00d6\u011f\u00fcd\u00fcc\u00fc, \u015e.G. (2015). Predicting community evolution based on time series modeling. In Proceedings of the 2015 IEEE\/ACM international conference on advances in social networks analysis and mining 2015 (pp. 1509\u20131516).","DOI":"10.1145\/2808797.2808913"},{"key":"6600_CR17","doi-asserted-by":"crossref","unstructured":"Kalnis, P., Mamoulis, N., & Bakiras, S. (2005). On discovering moving clusters in spatio-temporal data. In Advances in spatial and temporal databases: 9th international symposium, SSTD 2005, Proceedings 9 (pp. 364\u2013381). Springer.","DOI":"10.1007\/11535331_21"},{"key":"6600_CR18","volume-title":"Spatio-temporal clustering","author":"S Kisilevich","year":"2010","unstructured":"Kisilevich, S., Mansmann, F., Nanni, M., & Rinzivillo, S. (2010). Spatio-temporal clustering. Springer."},{"issue":"3","key":"6600_CR19","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s12530-012-9046-5","volume":"3","author":"E Lughofer","year":"2012","unstructured":"Lughofer, E. (2012). A dynamic split-and-merge approach for evolving cluster models. Evolving Systems, 3(3), 135\u2013151.","journal-title":"Evolving Systems"},{"key":"6600_CR20","unstructured":"MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability (Vol. 1, pp. 281\u2013297)."},{"issue":"9","key":"6600_CR21","doi-asserted-by":"publisher","first-page":"0137502","DOI":"10.1371\/journal.pone.0137502","volume":"10","author":"R Mall","year":"2015","unstructured":"Mall, R., Langone, R., & Suykens, J. A. (2015). Netgram: Visualizing communities in evolving networks. PloS One, 10(9), 0137502.","journal-title":"PloS One"},{"issue":"9","key":"6600_CR22","doi-asserted-by":"publisher","first-page":"0136497","DOI":"10.1371\/journal.pone.0136497","volume":"10","author":"R Mastrandrea","year":"2015","unstructured":"Mastrandrea, R., Fournet, J., & Barrat, A. (2015). Contact patterns in a high school: A comparison between data collected using wearable sensors, contact diaries and friendship surveys. PloS One, 10(9), 0136497.","journal-title":"PloS One"},{"key":"6600_CR23","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.tcs.2021.01.013","volume":"859","author":"PR Morales","year":"2021","unstructured":"Morales, P. R., Lamarche-Perrin, R., Fournier-S\u2019Niehotta, R., Poulain, R., Tabourier, L., & Tarissan, F. (2021). Measuring diversity in heterogeneous information networks. Theoretical Computer Science, 859, 80\u2013115.","journal-title":"Theoretical Computer Science"},{"issue":"7136","key":"6600_CR24","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1038\/nature05670","volume":"446","author":"G Palla","year":"2007","unstructured":"Palla, G., Barab\u00e1si, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664\u2013667.","journal-title":"Nature"},{"key":"6600_CR25","doi-asserted-by":"crossref","unstructured":"Peel, L., & Clauset, A. (2015). Detecting change points in the large-scale structure of evolving networks. In Proceedings of the AAAI conference on artificial intelligence (Vol. 29).","DOI":"10.1609\/aaai.v29i1.9574"},{"key":"6600_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecocom.2020.100904","volume":"45","author":"LR Pereira","year":"2021","unstructured":"Pereira, L. R., Lopes, R. J., & Louca, J. (2021). Community identity in a temporal network: A taxonomy proposal. Ecological Complexity, 45, 100904.","journal-title":"Ecological Complexity"},{"issue":"3","key":"6600_CR27","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1037\/0096-3445.104.3.192","volume":"104","author":"E Rosch","year":"1975","unstructured":"Rosch, E. (1975). Cognitive representations of semantic categories. Journal of Experimental Psychology: General, 104(3), 192.","journal-title":"Journal of Experimental Psychology: General"},{"issue":"2","key":"6600_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3172867","volume":"51","author":"G Rossetti","year":"2018","unstructured":"Rossetti, G., & Cazabet, R. (2018). Community discovery in dynamic networks: A survey. ACM Computing Surveys (CSUR), 51(2), 1\u201337.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"6600_CR29","doi-asserted-by":"crossref","unstructured":"Saganowski, S. (2015). Predicting community evolution in social networks. In Proceedings of the 2015 IEEE\/ACM international conference on advances in social networks analysis and mining 2015 (pp. 924\u2013925).","DOI":"10.1145\/2808797.2809353"},{"issue":"8","key":"6600_CR30","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.-F., Quaggiotto, M., Broeck, W., R\u00e9gis, C., & Lina, B. (2011). High-resolution measurements of face-to-face contact patterns in a primary school. PloS One, 6(8), 23176.","journal-title":"PloS One"},{"key":"6600_CR31","doi-asserted-by":"crossref","unstructured":"Sun, Y., Tang, J., Pan, L., & Li, J. (2015). Matrix based community evolution events detection in online social networks. In 2015 IEEE international conference on smart city\/SocialCom\/SustainCom (SmartCity) (pp. 465\u2013470). IEEE.","DOI":"10.1109\/SmartCity.2015.114"},{"key":"6600_CR32","doi-asserted-by":"crossref","unstructured":"Tsoukanara, E., Koloniari, G., & Pitoura, E. (2021). Should I stay or should I go: Predicting changes in cluster membership. In Web and big data. APWeb-WAIM 2021 international workshops: KGMA 2021, SemiBDMA 2021, DeepLUDA 2021, Revised Selected Papers 5 (pp. 3\u201315). Springer.","DOI":"10.1007\/978-981-16-8143-1_1"},{"issue":"9","key":"6600_CR33","doi-asserted-by":"publisher","first-page":"73970","DOI":"10.1371\/journal.pone.0073970","volume":"8","author":"P Vanhems","year":"2013","unstructured":"Vanhems, P., Barrat, A., Cattuto, C., Pinton, J.-F., Khanafer, N., R\u00e9gis, C., Kim, B.-A., Comte, B., & Voirin, N. (2013). Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PloS One, 8(9), 73970.","journal-title":"PloS One"},{"key":"6600_CR34","doi-asserted-by":"crossref","unstructured":"Vinh, N., Epps, J., & Bailey, J. (2009). Information theoretic measures for clusterings comparison: Variants. Properties, Normalization and Correction for Chance,18.","DOI":"10.1145\/1553374.1553511"},{"issue":"2","key":"6600_CR35","doi-asserted-by":"publisher","first-page":"1201","DOI":"10.1007\/s10462-020-09874-x","volume":"54","author":"A Zubaro\u011flu","year":"2021","unstructured":"Zubaro\u011flu, A., & Atalay, V. (2021). Data stream clustering: A review. Artificial Intelligence Review, 54(2), 1201\u20131236.","journal-title":"Artificial Intelligence Review"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-024-06600-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10994-024-06600-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-024-06600-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,17]],"date-time":"2024-10-17T21:09:23Z","timestamp":1729199363000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10994-024-06600-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":35,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["6600"],"URL":"https:\/\/doi.org\/10.1007\/s10994-024-06600-4","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"assertion":[{"value":"11 March 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 August 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}