{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T11:14:04Z","timestamp":1772622844956,"version":"3.50.1"},"reference-count":153,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"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":["Rev Socionetwork Strat"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1007\/s12626-025-00187-5","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T19:24:30Z","timestamp":1753298670000},"page":"183-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Review of Temporal Networks: Operations and Applications"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-4505-3757","authenticated-orcid":false,"given":"Aaqib","family":"Zahoor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Iqra Altaf","family":"Gillani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janib ul","family":"Bashir","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"187_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjb\/e2015-60657-4","volume":"88","author":"P Holme","year":"2015","unstructured":"Holme, P. (2015). Modern temporal network theory: A colloquium. The European Physical Journal B, 88, 1\u201330.","journal-title":"The European Physical Journal B"},{"issue":"3","key":"187_CR2","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.physrep.2012.03.001","volume":"519","author":"P Holme","year":"2012","unstructured":"Holme, P., & Saram\u00e4ki, J. (2012). Temporal networks. Physics Reports, 519(3), 97\u2013125.","journal-title":"Physics Reports"},{"key":"187_CR3","doi-asserted-by":"crossref","unstructured":"Batagelj, V., Doreian, P., Ferligoj, A., & Kejzar, N. (2014) Understanding large temporal networks and spatial networks: Exploration, pattern searching, visualization and network evolution, vol.\u00a02. John Wiley & Sons.","DOI":"10.1002\/9781118915370"},{"key":"187_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.is.2015.02.002","volume":"51","author":"D Caro","year":"2015","unstructured":"Caro, D., Rodr\u00edguez, M. A., & Brisaboa, N. R. (2015). Data structures for temporal graphs based on compact sequence representations. Information Systems, 51, 1\u201326.","journal-title":"Information Systems"},{"key":"187_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.tcs.2016.04.006","volume":"634","author":"O Michail","year":"2016","unstructured":"Michail, O., & Spirakis, P. G. (2016). Traveling salesman problems in temporal graphs. Theoretical Computer Science, 634, 1\u201323.","journal-title":"Theoretical Computer Science"},{"key":"187_CR6","doi-asserted-by":"crossref","unstructured":"Bumpus, B.M., & Meeks, K. (2022). Edge exploration of temporal graphs. Algorithmica, 1\u201329.","DOI":"10.1007\/s00453-022-01018-7"},{"key":"187_CR7","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1016\/j.jcss.2019.08.002","volume":"107","author":"EC Akrida","year":"2020","unstructured":"Akrida, E. C., Mertzios, G. B., Spirakis, P. G., & Zamaraev, V. (2020). Temporal vertex cover with a sliding time window. Journal of Computer and System Sciences, 107, 108\u2013123.","journal-title":"Journal of Computer and System Sciences"},{"key":"187_CR8","doi-asserted-by":"crossref","unstructured":"Huang, S., Fu, A.W.-C., & Liu, R. (2015). Minimum spanning trees in temporal graphs. In Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, 419\u2013430.","DOI":"10.1145\/2723372.2723717"},{"key":"187_CR9","doi-asserted-by":"crossref","unstructured":"Gunturi, V., Shekhar, S., & Bhattacharya, A. (2010). Minimum spanning tree on spatio-temporal networks.","DOI":"10.1007\/978-3-642-15251-1_11"},{"issue":"23","key":"187_CR10","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.109.238701","volume":"109","author":"N Perra","year":"2012","unstructured":"Perra, N., Baronchelli, A., Mocanu, D., Gon\u00e7alves, B., Pastor-Satorras, R., & Vespignani, A. (2012). Random walks and search in time-varying networks. Physical Review Letters, 109(23), Article 238701.","journal-title":"Physical Review Letters"},{"key":"187_CR11","doi-asserted-by":"crossref","unstructured":"Nguyen, G.H., Lee, J.B., Rossi, R.A., Ahmed, N.K., Koh, E., & Kim, S. (2018). Dynamic network embeddings: From random walks to temporal random walks. In 2018 IEEE International Conference on Big Data (Big Data), pp.\u00a01085\u20131092, IEEE.","DOI":"10.1109\/BigData.2018.8622109"},{"issue":"5","key":"187_CR12","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.95.052318","volume":"95","author":"L Alessandretti","year":"2017","unstructured":"Alessandretti, L., Sun, K., Baronchelli, A., & Perra, N. (2017). Random walks on activity-driven networks with attractiveness. Physical Review E, 95(5), Article 052318.","journal-title":"Physical Review E"},{"issue":"5","key":"187_CR13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0155196","volume":"11","author":"HH Lentz","year":"2016","unstructured":"Lentz, H. H., Koher, A., H\u00f6vel, P., Gethmann, J., Sauter-Louis, C., Selhorst, T., & Conraths, F. J. (2016). Disease spread through animal movements: A static and temporal network analysis of pig trade in Germany. PloS One, 11(5), Article e0155196.","journal-title":"PloS One"},{"issue":"3","key":"187_CR14","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1103\/RevModPhys.87.925","volume":"87","author":"R Pastor-Satorras","year":"2015","unstructured":"Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925.","journal-title":"Reviews of Modern Physics"},{"key":"187_CR15","doi-asserted-by":"crossref","unstructured":"Masuda, N., & Lambiotte, R. (2016). A guide to temporal networks. World Scientific.","DOI":"10.1142\/q0033"},{"key":"187_CR16","unstructured":"Holme, P., & Saramki, J. (2021). A map of approaches to temporal networks."},{"key":"187_CR17","doi-asserted-by":"publisher","first-page":"08","DOI":"10.1140\/epjb\/e2015-60657-4","volume":"88","author":"P Holme","year":"2015","unstructured":"Holme, P. (2015). Modern temporal network theory: A colloquium. The European Physical Journal B, 88, 08.","journal-title":"The European Physical Journal B"},{"key":"187_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2014.07.001","volume":"544","author":"S Boccaletti","year":"2014","unstructured":"Boccaletti, S., Bianconi, G., Criado, R., del Genio, C., G\u00f3mez-Garde\u00f1es, J., Romance, M., Sendi\u00f1a-Nadal, I., Wang, Z., & Zanin, M. (2014). The structure and dynamics of multilayer networks. Physics Reports, 544, 1\u2013122.","journal-title":"Physics Reports"},{"key":"187_CR19","doi-asserted-by":"crossref","unstructured":"Wehmuth, K., Ziviani, A., & Fleury, E. (2014). A unifying model for representing time-varying graphs","DOI":"10.1109\/DSAA.2015.7344810"},{"key":"187_CR20","doi-asserted-by":"crossref","unstructured":"Bach, B., Pietriga, E., & Fekete, J.-D. (2014). Visualizing dynamic networks with matrix cubes. CHI \u201914, (New York, NY, USA), p.\u00a0877 886, Association for Computing Machinery.","DOI":"10.1145\/2556288.2557010"},{"key":"187_CR21","doi-asserted-by":"crossref","unstructured":"Krings, G., Karsai, M., Bernhardsson, S., Blondel, V.D., & Saramki, J. (2012). Effects of time window size and placement on the structure of an aggregated communication network. EPJ Data Science, vol.\u00a01, May.","DOI":"10.1140\/epjds4"},{"key":"187_CR22","doi-asserted-by":"publisher","first-page":"02","DOI":"10.1371\/journal.pcbi.1003142","volume":"9","author":"P Holme","year":"2013","unstructured":"Holme, P. (2013). Epidemiologically optimal static networks from temporal network data. PLoS Computational Biology, 9, 02.","journal-title":"PLoS Computational Biology"},{"key":"187_CR23","doi-asserted-by":"crossref","unstructured":"Neiger, V., Crespelle, C., & Fleury, E. (2012). On the structure of changes in dynamic contact networks. In 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems, pp.\u00a0731\u2013738.","DOI":"10.1109\/SITIS.2012.111"},{"key":"187_CR24","doi-asserted-by":"crossref","unstructured":"Rosvall, M., Esquivel, A.V., Lancichinetti, A., West, J.D., & Lambiotte, R (2014). Memory in network flows and its effects on spreading dynamics and community detection. Nature Communications, vol.\u00a05, Aug.","DOI":"10.1038\/ncomms5630"},{"key":"187_CR25","doi-asserted-by":"crossref","unstructured":"Kivel, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y., & Porter, M.A. (2014). Multilayer networks. Journal of Complex Networks, vol.\u00a02, pp.\u00a0203\u2013271, 07.","DOI":"10.1093\/comnet\/cnu016"},{"key":"187_CR26","doi-asserted-by":"crossref","unstructured":"Tang, J., Musolesi, M., Mascolo, C., Latora, V., & Nicosia, V. (2010) Analysing information flows and key mediators through temporal centrality metrics. In Proceedings of the 3rd Workshop on Social Network Systems, SNS \u201910, (New York, NY, USA), Association for Computing Machinery.","DOI":"10.1145\/1852658.1852661"},{"key":"187_CR27","doi-asserted-by":"crossref","unstructured":"Tang, J., Scellato, S., Musolesi, M., Mascolo, C., & Latora, V. (2010). Small-world behavior in time-varying graphs. Physical Review E, vol.\u00a081.","DOI":"10.1103\/PhysRevE.81.055101"},{"key":"187_CR28","doi-asserted-by":"crossref","unstructured":"Nicosia, V., Tang, J., Mascolo, C., Musolesi, M., Russo, G., & Latora, V. (2013). Graph metrics for temporal networks. Temporal Networks, pp.\u00a015\u201340.","DOI":"10.1007\/978-3-642-36461-7_2"},{"key":"187_CR29","doi-asserted-by":"crossref","unstructured":"Gionis, A., Oettershagen, L., & Sarpe, I. (2024). Mining temporal networks. In Companion Proceedings of the ACM Web Conference 2024, WWW \u201924, (New York, NY, USA), p.\u00a01260 1263, Association for Computing Machinery.","DOI":"10.1145\/3589335.3641245"},{"issue":"5594","key":"187_CR30","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1126\/science.298.5594.824","volume":"298","author":"R Milo","year":"2002","unstructured":"Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002). Network motifs: Simple building blocks of complex networks. Science, 298(5594), 824\u2013827.","journal-title":"Science"},{"key":"187_CR31","doi-asserted-by":"crossref","unstructured":"Kovanen, L., Karsai, M., Kaski, K., Kert\u00e9sz, J., & Saram, K. (2011). Temporal motifs in time-dependent networks. Journal of Statistical Mechanics Theory and Experiment,2011, P11005.","DOI":"10.1088\/1742-5468\/2011\/11\/P11005"},{"key":"187_CR32","doi-asserted-by":"publisher","first-page":"i171","DOI":"10.1093\/bioinformatics\/btv227","volume":"31","author":"Y Hulovatyy","year":"2015","unstructured":"Hulovatyy, Y., Chen, H., & Milenkovic, T. (2015). Exploring the structure and function of temporal networks with dynamic graphlets. Bioinformatics, 31, i171\u2013i180, 06.","journal-title":"Bioinformatics"},{"key":"187_CR33","doi-asserted-by":"crossref","unstructured":"Nguyen, G.H., Lee, J.B., Rossi, R.A., Ahmed, N.K., Koh, E., & Kim, S. (2018). Dynamic network embeddings: From random walks to temporal random walks. In 2018 IEEE International Conference on Big Data (Big Data), pp.\u00a01085\u20131092, IEEE.","DOI":"10.1109\/BigData.2018.8622109"},{"key":"187_CR34","doi-asserted-by":"crossref","unstructured":"Zuo, Y., Liu, G., Lin, H., Guo, J., Hu, X., & Wu, J. (2018). Embedding temporal network via neighborhood formation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp.\u00a02857\u20132866.","DOI":"10.1145\/3219819.3220054"},{"key":"187_CR35","doi-asserted-by":"crossref","unstructured":"Bueno, M.L., Hommersom, A., & Lucas, P.J. (2020) Temporal exceptional model mining using dynamic bayesian networks. In Advanced Analytics and Learning on Temporal Data: 5th ECML PKDD Workshop, AALTD 2020, Ghent, Belgium, September 18, 2020, Revised Selected Papers 6, pp.\u00a097\u2013112, Springer.","DOI":"10.1007\/978-3-030-65742-0_7"},{"issue":"3","key":"187_CR36","first-page":"3145","volume":"35","author":"Z Qiu","year":"2021","unstructured":"Qiu, Z., Wu, J., Hu, W., Du, B., Yuan, G., & Yu, P. S. (2021). Temporal link prediction with motifs for social networks. IEEE Transactions on Knowledge and Data Engineering, 35(3), 3145\u20133158.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"187_CR37","doi-asserted-by":"crossref","unstructured":"Sarpe, I., Vandin, F., & Gionis, A. (2024).Scalable temporal motif densest subnetwork discovery. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.\u00a02536\u20132547.","DOI":"10.1145\/3637528.3671889"},{"key":"187_CR38","doi-asserted-by":"crossref","unstructured":"Chen, H., Ma, S., Liu, J., & Cui, L. (2025). Discovery of temporal network motifs. IEEE Transactions on Knowledge and Data Engineering.","DOI":"10.1109\/TKDE.2025.3538514"},{"issue":"1","key":"187_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s44248-025-00032-8","volume":"3","author":"AE Sar\u0131y\u00fcce","year":"2025","unstructured":"Sar\u0131y\u00fcce, A. E. (2025). A powerful lens for temporal network analysis: Temporal motifs. Discover Data, 3(1), 1\u201322.","journal-title":"Discover Data"},{"key":"187_CR40","doi-asserted-by":"crossref","unstructured":"Tantipathananandh, C., & Berger-Wolf, T.Y. (2011). Finding communities in dynamic social networks. In 2011 IEEE 11th international conference on data mining, pp.\u00a01236\u20131241, IEEE.","DOI":"10.1109\/ICDM.2011.67"},{"key":"187_CR41","unstructured":"Chen, J., Molter, H., Sorge, M., & Suchy, O. (2017). Cluster editing for multi-layer and temporal graphs. arXiv:1709.09100."},{"key":"187_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2019.04.001","volume":"813","author":"MS Mariani","year":"2019","unstructured":"Mariani, M. S., Ren, Z.-M., Bascompte, J., & Tessone, C. J. (2019). Nestedness in complex networks: Observation, emergence, and implications. Physics Reports, 813, 1\u201390.","journal-title":"Physics Reports"},{"issue":"3","key":"187_CR43","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3046791","volume":"11","author":"P Rozenshtein","year":"2017","unstructured":"Rozenshtein, P., Tatti, N., & Gionis, A. (2017). Finding dynamic dense subgraphs. ACM Transactions on Knowledge Discovery from Data (TKDD), 11(3), 1\u201330.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"187_CR44","doi-asserted-by":"crossref","unstructured":"Bogdanov, P., Mongiov, M., & Singh, A.K. (2011). Mining heavy subgraphs in time-evolving networks. In 2011 IEEE 11th International Conference on Data Mining, pp.\u00a081\u201390.","DOI":"10.1109\/ICDM.2011.101"},{"key":"187_CR45","doi-asserted-by":"crossref","unstructured":"Ma, S., Hu, R., Wang, L., Lin, X., & Huai, J. (2017). Fast computation of dense temporal subgraphs. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp.\u00a0361\u2013372, IEEE.","DOI":"10.1109\/ICDE.2017.95"},{"key":"187_CR46","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.jcss.2021.01.007","volume":"119","author":"J Enright","year":"2021","unstructured":"Enright, J., Meeks, K., Mertzios, G. B., & Zamaraev, V. (2021). Deleting edges to restrict the size of an epidemic in temporal networks. Journal of Computer and System Sciences, 119, 60\u201377.","journal-title":"Journal of Computer and System Sciences"},{"key":"187_CR47","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1007\/s00453-017-0311-7","volume":"80","author":"J Enright","year":"2018","unstructured":"Enright, J., & Meeks, K. (2018). Deleting edges to restrict the size of an epidemic: A new application for treewidth. Algorithmica, 80, 1857\u20131889.","journal-title":"Algorithmica"},{"issue":"5","key":"187_CR48","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.85.056115","volume":"85","author":"M Starnini","year":"2012","unstructured":"Starnini, M., Baronchelli, A., Barrat, A., & Pastor-Satorras, R. (2012). Random walks on temporal networks. Physical Review E, 85(5), Article 056115.","journal-title":"Physical Review E"},{"key":"187_CR49","doi-asserted-by":"crossref","unstructured":"Barros, C.D.T., Mendona, M.R.F., Vieira, A.B., & Ziviani, A. (2021). A survey on embedding dynamic graphs.","DOI":"10.1145\/3483595"},{"issue":"1","key":"187_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0169-5","volume":"4","author":"F B\u00e9res","year":"2019","unstructured":"B\u00e9res, F., Kelen, D. M., P\u00e1lovics, R., & Bencz\u00far, A. A. (2019). Node embeddings in dynamic graphs. Applied Network Science, 4(1), 1\u201325.","journal-title":"Applied Network Science"},{"key":"187_CR51","doi-asserted-by":"crossref","unstructured":"Zuo, Y., Liu, G., Lin, H., Guo, J., Hu, X., & Wu, J. (2018). Embedding temporal network via neighborhood formation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining, pp.\u00a02857\u20132866.","DOI":"10.1145\/3219819.3220054"},{"key":"187_CR52","unstructured":"Axiotis, K., & Fotakis, D. (2016). On the size and the approximability of minimum temporally connected subgraphs. arXiv:1602.06411."},{"issue":"8","key":"187_CR53","doi-asserted-by":"publisher","first-page":"2285","DOI":"10.1109\/TNNLS.2018.2881459","volume":"30","author":"B Li","year":"2018","unstructured":"Li, B., Lu, J., Zhong, J., & Liu, Y. (2018). Fast-time stability of temporal boolean networks. IEEE Transactions on Neural Networks and Learning Systems, 30(8), 2285\u20132294.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"187_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113642","volume":"159","author":"N Hafiene","year":"2020","unstructured":"Hafiene, N., Karoui, W., & Romdhane, L. B. (2020). Influential nodes detection in dynamic social networks: A survey. Expert Systems with Applications, 159, Article 113642.","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"187_CR55","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.102.042307","volume":"102","author":"\u015e Erkol","year":"2020","unstructured":"Erkol, \u015e, Mazzilli, D., & Radicchi, F. (2020). Influence maximization on temporal networks. Physical Review E, 102(4), Article 042307.","journal-title":"Physical Review E"},{"key":"187_CR56","doi-asserted-by":"crossref","unstructured":"Vega-Oliveros, D. A., da Fontoura Costa, L., & Rodrigues, F. A. (2020). Influence maximization by rumor spreading on correlated networks through community identification. Communications in Nonlinear Science and Numerical Simulation,83, Article 105094.","DOI":"10.1016\/j.cnsns.2019.105094"},{"issue":"1","key":"187_CR57","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1109\/TNET.2016.2563397","volume":"25","author":"G Tong","year":"2016","unstructured":"Tong, G., Wu, W., Tang, S., & Du, D.-Z. (2016). Adaptive influence maximization in dynamic social networks. IEEE\/ACM Transactions on Networking, 25(1), 112\u2013125.","journal-title":"IEEE\/ACM Transactions on Networking"},{"issue":"1","key":"187_CR58","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1007\/s42979-023-02453-1","volume":"5","author":"S Dizaji","year":"2024","unstructured":"Dizaji, S., Patil, K., & Avrachenkov, K. (2024). Influence maximization in dynamic networks using reinforcement learning. SN Computer Science, 5(1), 169.","journal-title":"SN Computer Science"},{"key":"187_CR59","doi-asserted-by":"crossref","unstructured":"Michalski, R., Jankowski, J., & Pazura, P. (2020). Entropy-based measure for influence maximization in temporal networks. in International Conference on Computational Science, pp.\u00a0277\u2013290, Springer.","DOI":"10.1007\/978-3-030-50423-6_21"},{"key":"187_CR60","doi-asserted-by":"crossref","unstructured":"Zahoor, A., Gillani, I.A. et\u00a0al. (2024). Influence maximization in temporal networks with persistent and reactive behaviors. arXiv:2412.20936.","DOI":"10.2139\/ssrn.5223111"},{"key":"187_CR61","doi-asserted-by":"crossref","unstructured":"Zahoor, A., Gillani, I.A., & Bashir, J. (2024). Tbcelf: Temporal budget-aware influence maximization. in Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), pp.\u00a0580\u2013581.","DOI":"10.1145\/3632410.3632485"},{"issue":"1","key":"187_CR62","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1007\/s41109-024-00625-3","volume":"9","author":"E Yanchenko","year":"2024","unstructured":"Yanchenko, E., Murata, T., & Holme, P. (2024). Influence maximization on temporal networks: A review. Applied Network Science, 9(1), 16.","journal-title":"Applied Network Science"},{"key":"187_CR63","doi-asserted-by":"crossref","unstructured":"Yang, C., Wang, C., Lu, Y., Gong, X., Shi, C., Wang, W., & Zhang, X. (2022). Few-shot link prediction in dynamic networks. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp.\u00a01245\u20131255.","DOI":"10.1145\/3488560.3498417"},{"issue":"12","key":"187_CR64","doi-asserted-by":"publisher","first-page":"4946","DOI":"10.1109\/TCYB.2019.2920268","volume":"50","author":"M Yang","year":"2019","unstructured":"Yang, M., Liu, J., Chen, L., Zhao, Z., Chen, X., & Shen, Y. (2019). An advanced deep generative framework for temporal link prediction in dynamic networks. IEEE Transactions on Cybernetics, 50(12), 4946\u20134957.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"187_CR65","doi-asserted-by":"crossref","unstructured":"Das, S., & Das, S.K. (2017). A probabilistic link prediction model in time-varying social networks. In 2017 IEEE International Conference on Communications (ICC), pp.\u00a01\u20136, IEEE.","DOI":"10.1109\/ICC.2017.7996909"},{"issue":"2","key":"187_CR66","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1921632.1921636","volume":"5","author":"DM Dunlavy","year":"2011","unstructured":"Dunlavy, D. M., Kolda, T. G., & Acar, E. (2011). Temporal link prediction using matrix and tensor factorizations. ACM Transactions on Knowledge Discovery from Data (TKDD), 5(2), 1\u201327.","journal-title":"ACM Transactions on Knowledge Discovery from Data (TKDD)"},{"key":"187_CR67","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2016.09.029","volume":"374","author":"NM Ahmed","year":"2016","unstructured":"Ahmed, N. M., Chen, L., Wang, Y., Li, B., Li, Y., & Liu, W. (2016). Sampling-based algorithm for link prediction in temporal networks. Information Sciences, 374, 1\u201314.","journal-title":"Information Sciences"},{"key":"187_CR68","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s11280-017-0463-z","volume":"21","author":"N Sett","year":"2018","unstructured":"Sett, N., Basu, S., Nandi, S., & Singh, S. R. (2018). Temporal link prediction in multi-relational network. World Wide Web, 21, 395\u2013419.","journal-title":"World Wide Web"},{"key":"187_CR69","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1016\/j.patcog.2017.06.025","volume":"71","author":"X Ma","year":"2017","unstructured":"Ma, X., Sun, P., & Qin, G. (2017). Nonnegative matrix factorization algorithms for link prediction in temporal networks using graph communicability. Pattern Recognition, 71, 361\u2013374.","journal-title":"Pattern Recognition"},{"issue":"3","key":"187_CR70","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1109\/TNSE.2021.3138643","volume":"9","author":"L Zou","year":"2021","unstructured":"Zou, L., Zhan, X.-X., Sun, J., Hanjalic, A., & Wang, H. (2021). Temporal network prediction and interpretation. IEEE Transactions on Network Science and Engineering, 9(3), 1215\u20131224.","journal-title":"IEEE Transactions on Network Science and Engineering"},{"issue":"4","key":"187_CR71","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3625820","volume":"56","author":"M Qin","year":"2023","unstructured":"Qin, M., & Yeung, D.-Y. (2023). Temporal link prediction: A unified framework, taxonomy, and review. ACM Computing Surveys, 56(4), 1\u201340.","journal-title":"ACM Computing Surveys"},{"key":"187_CR72","unstructured":"Xiong, J., Zareie, A., & Sakellariou, R. (2025). A survey of link prediction in temporal networks. arXiv:2502.21185."},{"key":"187_CR73","doi-asserted-by":"crossref","unstructured":"Dhote, Y., Mishra, N., & Sharma, S.K. (2013). Survey and analysis of temporal link prediction in online social networks. 2013 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp.\u00a01178\u20131183.","DOI":"10.1109\/ICACCI.2013.6637344"},{"key":"187_CR74","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. ACM Computing Surveys, 51, 1\u201337.","journal-title":"ACM Computing Surveys"},{"key":"187_CR75","doi-asserted-by":"crossref","unstructured":"Akoglu, L., Tong, H., & Koutra, D. (2014). Graph-based anomaly detection and description: A survey.","DOI":"10.1007\/s10618-014-0365-y"},{"key":"187_CR76","doi-asserted-by":"crossref","unstructured":"Rozenshtein, P., Anagnostopoulos, A., Gionis, A., & Tatti, N. (2014). Event detection in activity networks. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD\u201914, (F\u00f6renta Staterna (USA)), ACM Press.","DOI":"10.1145\/2623330.2623674"},{"key":"187_CR77","doi-asserted-by":"crossref","unstructured":"Rozenshtein, P., & Gionis, A. (2019). Mining temporal networks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201919, (New York, NY, USA), p.\u00a03225 3226, Association for Computing Machinery.","DOI":"10.1145\/3292500.3332295"},{"issue":"5","key":"187_CR78","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0036439","volume":"7","author":"S Lee","year":"2012","unstructured":"Lee, S., Rocha, L., Liljeros, F., & Holme, P. (2012). Exploiting temporal network structures of human interaction to effectively immunize populations. PloS One, 7(5), Article e36439.","journal-title":"PloS One"},{"key":"187_CR79","unstructured":"Rodriguez, M.G., & Sch lkopf, B. (2012). Influence maximization in continuous time diffusion networks."},{"key":"187_CR80","doi-asserted-by":"crossref","unstructured":"Rozenshtein, P., Gionis, A., Prakash, B.A., & Vreeken, J. (2016). Reconstructing an epidemic over time. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD \u201916, (New York, NY, USA), p.\u00a01835 1844, Association for Computing Machinery.","DOI":"10.1145\/2939672.2939865"},{"issue":"2","key":"187_CR81","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.90.022812","volume":"90","author":"V Kohar","year":"2014","unstructured":"Kohar, V., Ji, P., Choudhary, A., Sinha, S., & Kurths, J. (2014). Synchronization in time-varying networks. Physical Review E, 90(2), Article 022812.","journal-title":"Physical Review E"},{"issue":"1","key":"187_CR82","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1016\/j.physd.2004.03.012","volume":"195","author":"VN Belykh","year":"2004","unstructured":"Belykh, V. N., Belykh, I. V., & Hasler, M. (2004). Connection graph stability method for synchronized coupled chaotic systems. Physica D: Nonlinear Phenomena, 195(1), 159\u2013187.","journal-title":"Physica D: Nonlinear Phenomena"},{"key":"187_CR83","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.90.022812","volume":"90","author":"V Kohar","year":"2014","unstructured":"Kohar, V., Ji, P., Choudhary, A., Sinha, S., & Kurths, J. (2014). Synchronization in time-varying networks. Physical Review E, 90, Article 022812.","journal-title":"Physical Review E"},{"issue":"6","key":"187_CR84","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TAC.2005.849233","volume":"50","author":"J Lu","year":"2005","unstructured":"Lu, J., & Chen, G. (2005). A time-varying complex dynamical network model and its controlled synchronization criteria. IEEE Transactions on Automatic Control, 50(6), 841\u2013846.","journal-title":"IEEE Transactions on Automatic Control"},{"key":"187_CR85","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.7.011028","volume":"7","author":"D Levis","year":"2017","unstructured":"Levis, D., Pagonabarraga, I., & D\u00edaz-Guilera, A. (2017). Synchronization in dynamical networks of locally coupled self-propelled oscillators. Physical Review X, 7, Article 011028.","journal-title":"Physical Review X"},{"key":"187_CR86","unstructured":"Lee, J.B., Nguyen, G., Rossi, R.A., Ahmed, N.K., Koh, E., & Kim, S. (2019). Temporal network representation learning. arXiv:1904.06449."},{"key":"187_CR87","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1016\/j.dam.2022.01.017","volume":"319","author":"A Mohan","year":"2022","unstructured":"Mohan, A., & Pramod, K. (2022). Representation learning for temporal networks using temporal random walk and deep autoencoder. Discrete Applied Mathematics, 319, 595\u2013605.","journal-title":"Discrete Applied Mathematics"},{"key":"187_CR88","doi-asserted-by":"crossref","unstructured":"Wu, H., Cheng, J., Lu, Y., Ke, Y., Huang, Y., Yan, D., & Wu, H. (2015). Core decomposition in large temporal graphs. In 2015 IEEE International Conference on Big Data (Big Data), pp.\u00a0649\u2013658, IEEE.","DOI":"10.1109\/BigData.2015.7363809"},{"key":"187_CR89","unstructured":"Mertzios, G.B., Molter, H., Niedermeier, R., Zamaraev, V., & Zschoche, P. (2019). Computing maximum matchings in temporal graphs. arXiv:1905.05304."},{"key":"187_CR90","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s11280-019-00674-0","volume":"23","author":"T Zhang","year":"2020","unstructured":"Zhang, T., Gao, Y., Qiu, L., Chen, L., Linghu, Q., & Pu, S. (2020). Distributed time-respecting flow graph pattern matching on temporal graphs. World Wide Web, 23, 609\u2013630.","journal-title":"World Wide Web"},{"issue":"9","key":"187_CR91","doi-asserted-by":"publisher","first-page":"211","DOI":"10.3390\/a13090211","volume":"13","author":"P Crescenzi","year":"2020","unstructured":"Crescenzi, P., Magnien, C., & Marino, A. (2020). Finding top-k nodes for temporal closeness in large temporal graphs. Algorithms, 13(9), 211.","journal-title":"Algorithms"},{"key":"187_CR92","doi-asserted-by":"crossref","unstructured":"Rozenshtein, P., & Gionis, A. (2016). Temporal pagerank. In Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II 16, pp.\u00a0674\u2013689, Springer.","DOI":"10.1007\/978-3-319-46227-1_42"},{"issue":"3","key":"187_CR93","doi-asserted-by":"publisher","DOI":"10.1063\/1.5086059","volume":"29","author":"C Qu","year":"2019","unstructured":"Qu, C., Zhan, X., Wang, G., Wu, J., & Zhang, Z.-K. (2019). Temporal information gathering process for node ranking in time-varying networks. Chaos: An Interdisciplinary Journal of Nonlinear Science, 29(3), Article 033116.","journal-title":"Chaos: An Interdisciplinary Journal of Nonlinear Science"},{"key":"187_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjb\/e2016-60498-7","volume":"89","author":"T Takaguchi","year":"2016","unstructured":"Takaguchi, T., Yano, Y., & Yoshida, Y. (2016). Coverage centralities for temporal networks. The European Physical Journal B, 89, 1\u201311.","journal-title":"The European Physical Journal B"},{"issue":"1","key":"187_CR95","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-016-0028-x","volume":"7","author":"D-W Huang","year":"2017","unstructured":"Huang, D.-W., & Yu, Z.-G. (2017). Dynamic-sensitive centrality of nodes in temporal networks. Scientific Reports, 7(1), 1\u201311.","journal-title":"Scientific Reports"},{"key":"187_CR96","first-page":"1640","volume":"2022","author":"L Oettershagen","year":"2022","unstructured":"Oettershagen, L., Mutzel, P., & Kriege, N. M. (2022). Temporal walk centrality: Ranking nodes in evolving networks. Proceedings of the ACM Web conference, 2022, 1640\u20131650.","journal-title":"Proceedings of the ACM Web conference"},{"key":"187_CR97","doi-asserted-by":"crossref","unstructured":"Oettershagen, L., Kriege, N.M., & Mutzel, P. (2023). A higher-order temporal h-index for evolving networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.\u00a01770\u20131782.","DOI":"10.1145\/3580305.3599242"},{"key":"187_CR98","doi-asserted-by":"crossref","unstructured":"Bu\u00df, S., Molter, H., Niedermeier, R., & Rymar, M. (2020). Algorithmic aspects of temporal betweenness. In Proceedings of the 26th ACM SIGKDD International conference on knowledge discovery and data mining, pp.\u00a02084\u20132092.","DOI":"10.1145\/3394486.3403259"},{"key":"187_CR99","doi-asserted-by":"crossref","unstructured":"Brunelli, F., Crescenzi, P., & Viennot, L. (2024). Making temporal betweenness computation faster and restless. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp.\u00a0163\u2013174.","DOI":"10.1145\/3637528.3671825"},{"key":"187_CR100","doi-asserted-by":"crossref","unstructured":"Pellegrina, L. (2023). Efficient centrality maximization with rademacher averages. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 1872\u20131884.","DOI":"10.1145\/3580305.3599325"},{"key":"187_CR101","doi-asserted-by":"crossref","unstructured":"Cruciani, A. (2024) Mantra: Temporal betweenness centrality approximation through sampling. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.\u00a0125\u2013143, Springer.","DOI":"10.1007\/978-3-031-70341-6_8"},{"key":"187_CR102","first-page":"2395","volume":"2024","author":"T Zhang","year":"2024","unstructured":"Zhang, T., Gao, Y., Zhao, J., Chen, L., Jin, L., Yang, Z., Cao, B., & Fan, J. (2024). Efficient exact and approximate betweenness centrality computation for temporal graphs. Proceedings of the ACM Web Conference, 2024, 2395\u20132406.","journal-title":"Proceedings of the ACM Web Conference"},{"key":"187_CR103","doi-asserted-by":"crossref","unstructured":"Himmel, A.-S., Bentert, M., Nichterlein, A., & Niedermeier, R. (2019). Efficient computation of optimal temporal walks under waiting-time constraints. In Complex Networks and Their Applications VIII: Volume 2 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, pp.\u00a0494\u2013506, Springer.","DOI":"10.1007\/978-3-030-36683-4_40"},{"issue":"11","key":"187_CR104","doi-asserted-by":"publisher","first-page":"2927","DOI":"10.1109\/TKDE.2016.2594065","volume":"28","author":"H Wu","year":"2016","unstructured":"Wu, H., Cheng, J., Ke, Y., Huang, S., Huang, Y., & Wu, H. (2016). Efficient algorithms for temporal path computation. IEEE Transactions on Knowledge and Data Engineering, 28(11), 2927\u20132942.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"187_CR105","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.92.012818","volume":"92","author":"E Ser-Giacomi","year":"2015","unstructured":"Ser-Giacomi, E., Vasile, R., Hernandez-Garcia, E., & L\u00f3pez, C. (2015). Most probable paths in temporal weighted networks: An application to ocean transport. Physical Review E, 92(1), Article 012818.","journal-title":"Physical Review E"},{"key":"187_CR106","doi-asserted-by":"crossref","unstructured":"Rozenshtein, P., Gionis, A.: (2019). Mining temporal networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp.\u00a03225\u20133226.","DOI":"10.1145\/3292500.3332295"},{"key":"187_CR107","doi-asserted-by":"crossref","unstructured":"Bumpus, B.M., & Meeks, K. (2022). Edge exploration of temporal graphs. Algorithmica, 1\u201329.","DOI":"10.1007\/s00453-022-01018-7"},{"issue":"4","key":"187_CR108","doi-asserted-by":"publisher","first-page":"cnaa031","DOI":"10.1093\/comnet\/cnaa031","volume":"8","author":"A Jazayeri","year":"2020","unstructured":"Jazayeri, A., & Yang, C. C. (2020). Motif discovery algorithms in static and temporal networks: A survey. Journal of Complex Networks, 8(4), cnaa031.","journal-title":"Journal of Complex Networks"},{"key":"187_CR109","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-017-0455-0","volume":"7","author":"A-S Himmel","year":"2017","unstructured":"Himmel, A.-S., Molter, H., Niedermeier, R., & Sorge, M. (2017). Adapting the bron-kerbosch algorithm for enumerating maximal cliques in temporal graphs. Social Network Analysis and Mining, 7, 1\u201316.","journal-title":"Social Network Analysis and Mining"},{"key":"187_CR110","doi-asserted-by":"crossref","unstructured":"Himmel, A.-S., Molter, H., Niedermeier, R., & Sorge, M. (2016). Enumerating maximal cliques in temporal graphs. In 2016 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp.\u00a0337\u2013344, IEEE.","DOI":"10.1109\/ASONAM.2016.7752255"},{"key":"187_CR111","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3325859","volume":"24","author":"M Bentert","year":"2019","unstructured":"Bentert, M., Himmel, A.-S., Molter, H., Morik, M., Niedermeier, R., & Saitenmacher, R. (2019). Listing all maximal k-plexes in temporal graphs. Journal of Experimental Algorithmics (JEA), 24, 1\u201327.","journal-title":"Journal of Experimental Algorithmics (JEA)"},{"key":"187_CR112","doi-asserted-by":"publisher","first-page":"7667","DOI":"10.1609\/aaai.v33i01.33017667","volume":"33","author":"GB Mertzios","year":"2019","unstructured":"Mertzios, G. B., Molter, H., & Zamaraev, V. (2019). Sliding window temporal graph coloring. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 7667\u20137674.","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"187_CR113","doi-asserted-by":"crossref","unstructured":"Huang, H., Song, J., Lin, X., Ma, S., & Huai, J. (2016). Tgraph: A temporal graph data management system. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 2469\u20132472.","DOI":"10.1145\/2983323.2983335"},{"key":"187_CR114","doi-asserted-by":"crossref","unstructured":"Gheibi, S., Banerjee, T., Ranka, S., & Sahni, S. (2021). An effective data structure for contact sequence temporal graphs. In 2021 IEEE Symposium on Computers and Communications (ISCC), 1\u20138, IEEE.","DOI":"10.1109\/ISCC53001.2021.9631469"},{"issue":"6","key":"187_CR115","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.87.062810","volume":"87","author":"B Kotnis","year":"2013","unstructured":"Kotnis, B., & Kuri, J. (2013). Stochastic analysis of epidemics on adaptive time varying networks. Physical Review E, 87(6), Article 062810.","journal-title":"Physical Review E"},{"key":"187_CR116","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.physa.2015.03.062","volume":"432","author":"D Han","year":"2015","unstructured":"Han, D., Sun, M., & Li, D. (2015). Epidemic process on activity-driven modular networks. Physica A: Statistical Mechanics and its Applications, 432, 354\u2013362.","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"4","key":"187_CR117","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0151209","volume":"11","author":"A Koher","year":"2016","unstructured":"Koher, A., Lentz, H. H., H\u00f6vel, P., & Sokolov, I. M. (2016). Infections on temporal networks a matrix-based approach. PloS One, 11(4), Article e0151209.","journal-title":"PloS One"},{"key":"187_CR118","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjb\/e2015-60620-5","volume":"88","author":"E Valdano","year":"2015","unstructured":"Valdano, E., Poletto, C., & Colizza, V. (2015). Infection propagator approach to compute epidemic thresholds on temporal networks: Impact of immunity and of limited temporal resolution. The European Physical Journal B, 88, 1\u201311.","journal-title":"The European Physical Journal B"},{"issue":"1","key":"187_CR119","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.181404","volume":"6","author":"A Darbon","year":"2019","unstructured":"Darbon, A., Colombi, D., Valdano, E., Savini, L., Giovannini, A., & Colizza, V. (2019). Disease persistence on temporal contact networks accounting for heterogeneous infectious periods. Royal Society Open Science, 6(1), Article 181404.","journal-title":"Royal Society Open Science"},{"issue":"7","key":"187_CR120","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/18\/7\/073013","volume":"18","author":"L Speidel","year":"2016","unstructured":"Speidel, L., Klemm, K., Egu\u00edluz, V. M., & Masuda, N. (2016). Temporal interactions facilitate endemicity in the susceptible-infected-susceptible epidemic model. New Journal of Physics, 18(7), Article 073013.","journal-title":"New Journal of Physics"},{"key":"187_CR121","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejcon.2019.12.007","volume":"54","author":"L Zino","year":"2020","unstructured":"Zino, L., Rizzo, A., & Porfiri, M. (2020). Analysis and control of epidemics in temporal networks with self-excitement and behavioral changes. European Journal of Control, 54, 1\u201311.","journal-title":"European Journal of Control"},{"issue":"1","key":"187_CR122","doi-asserted-by":"publisher","first-page":"15511","DOI":"10.1038\/s41598-018-33313-1","volume":"8","author":"TP Peixoto","year":"2018","unstructured":"Peixoto, T. P., & Gauvin, L. (2018). Change points, memory and epidemic spreading in temporal networks. Scientific Reports, 8(1), 15511.","journal-title":"Scientific Reports"},{"issue":"3","key":"187_CR123","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1111\/tbed.13841","volume":"68","author":"G Machado","year":"2021","unstructured":"Machado, G., Galvis, J. A., Lopes, F., Voges, J., Medeiros, A., & C\u00e1rdenas, N. C. (2021). Quantifying the dynamics of pig movements improves targeted disease surveillance and control plans. Transboundary and Emerging Diseases, 68(3), 1663\u20131675.","journal-title":"Transboundary and Emerging Diseases"},{"issue":"5","key":"187_CR124","doi-asserted-by":"publisher","first-page":"50002","DOI":"10.1209\/0295-5075\/103\/50002","volume":"103","author":"B Min","year":"2013","unstructured":"Min, B., Goh, K.-I., & Kim, I.-M. (2013). Suppression of epidemic outbreaks with heavy-tailed contact dynamics. Europhysics Letters, 103(5), 50002.","journal-title":"Europhysics Letters"},{"key":"187_CR125","doi-asserted-by":"crossref","unstructured":"Elhesha, R., Sarkar, A., Boucher, C., & Kahveci, T. (2018). Identification of co-evolving temporal networks. In Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, pp.\u00a0591\u2013592.","DOI":"10.1145\/3233547.3233686"},{"issue":"4","key":"187_CR126","doi-asserted-by":"publisher","first-page":"2484","DOI":"10.1109\/TCBB.2021.3076961","volume":"19","author":"Q Li","year":"2021","unstructured":"Li, Q., & Milenkovi\u0107, T. (2021). Supervised prediction of aging-related genes from a context-specific protein interaction subnetwork. IEEE\/ACM Transactions on Computational Biology and Bioinformatics, 19(4), 2484\u20132498.","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"issue":"1","key":"187_CR127","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-021-04439-3","volume":"22","author":"Q Li","year":"2021","unstructured":"Li, Q., Newaz, K., & Milenkovi\u0107, T. (2021). Improved supervised prediction of aging-related genes via weighted dynamic network analysis. BMC Bioinformatics, 22(1), 1\u201326.","journal-title":"BMC Bioinformatics"},{"issue":"2","key":"187_CR128","doi-asserted-by":"publisher","first-page":"138","DOI":"10.1016\/j.icte.2021.05.006","volume":"7","author":"B Attanasio","year":"2021","unstructured":"Attanasio, B., La Corte, A., & Scat\u00e0, M. (2021). Evolutionary dynamics of mec s organization in a 6g scenario through egt and temporal multiplex social network. ICT Express, 7(2), 138\u2013142.","journal-title":"ICT Express"},{"key":"187_CR129","doi-asserted-by":"crossref","unstructured":"Paranjape, A., Benson, A.R., & Leskovec, J. (2017). Motifs in temporal networks. In Proceedings of the tenth ACM international conference on web search and data mining, pp.\u00a0601\u2013610.","DOI":"10.1145\/3018661.3018731"},{"issue":"1","key":"187_CR130","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/TKDE.2021.3077495","volume":"35","author":"P Liu","year":"2021","unstructured":"Liu, P., Guarrasi, V., & Sar\u0131y\u00fcce, A. E. (2021). Temporal network motifs: Models, limitations, evaluation. IEEE Transactions on Knowledge and Data Engineering, 35(1), 945\u2013957.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"187_CR131","doi-asserted-by":"publisher","first-page":"49778","DOI":"10.1109\/ACCESS.2019.2911181","volume":"7","author":"X Sun","year":"2019","unstructured":"Sun, X., Tan, Y., Wu, Q., Chen, B., & Shen, C. (2019). Tm-miner: Tfs-based algorithm for mining temporal motifs in large temporal network. IEEE Access, 7, 49778\u201349789.","journal-title":"IEEE Access"},{"key":"187_CR132","unstructured":"Chen, J., Molter, H., Sorge, M., & Suchy, O. (2017). Cluster editing for multi-layer and temporal graphs. arXiv:1709.09100."},{"key":"187_CR133","doi-asserted-by":"crossref","unstructured":"Ma, Y., Guo, Z., Ren, Z., Tang, J., & Yin, D. (2020). Streaming graph neural networks. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp.\u00a0719\u2013728.","DOI":"10.1145\/3397271.3401092"},{"issue":"1","key":"187_CR134","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/srep00469","volume":"2","author":"N Perra","year":"2012","unstructured":"Perra, N., Gon\u00e7alves, B., Pastor-Satorras, R., & Vespignani, A. (2012). Activity driven modeling of time varying networks. Scientific Reports, 2(1), 469.","journal-title":"Scientific Reports"},{"issue":"22","key":"187_CR135","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.121.228301","volume":"121","author":"G Petri","year":"2018","unstructured":"Petri, G., & Barrat, A. (2018). Simplicial activity driven model. Physical Review Letters, 121(22), Article 228301.","journal-title":"Physical Review Letters"},{"issue":"1","key":"187_CR136","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40649-018-0056-8","volume":"5","author":"T Murata","year":"2018","unstructured":"Murata, T., & Koga, H. (2018). Extended methods for influence maximization in dynamic networks. Computational Social Networks, 5(1), 1\u201321.","journal-title":"Computational Social Networks"},{"key":"187_CR137","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.ins.2019.05.050","volume":"498","author":"Z Bu","year":"2019","unstructured":"Bu, Z., Wang, Y., Li, H.-J., Jiang, J., Wu, Z., & Cao, J. (2019). Link prediction in temporal networks: Integrating survival analysis and game theory. Information Sciences, 498, 41\u201361.","journal-title":"Information Sciences"},{"key":"187_CR138","unstructured":"Rost, C., Thor, A., & Rahm, E. (2019). Temporal graph analysis using gradoop. BTW 2019\u2013Workshopband."},{"issue":"2","key":"187_CR139","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s00778-021-00667-4","volume":"31","author":"C Rost","year":"2022","unstructured":"Rost, C., Gomez, K., T\u00e4schner, M., Fritzsche, P., Schons, L., Christ, L., Adameit, T., Junghanns, M., & Rahm, E. (2022). Distributed temporal graph analytics with gradoop. The VLDB Journal, 31(2), 375\u2013401.","journal-title":"The VLDB Journal"},{"key":"187_CR140","doi-asserted-by":"crossref","unstructured":"Linhares, C.D., Traven\u00e7olo, B.A., Paiva, J.G.S., Rocha, L.E. (2017). Dynetvis: a system for visualization of dynamic networks. In Proceedings of the symposium on applied computing, pp.\u00a0187\u2013194.","DOI":"10.1145\/3019612.3019686"},{"key":"187_CR141","doi-asserted-by":"crossref","unstructured":"Rozenshtein, P., & Gionis, A. (2019). Mining temporal networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining, pp.\u00a03225\u20133226.","DOI":"10.1145\/3292500.3332295"},{"key":"187_CR142","doi-asserted-by":"crossref","unstructured":"Yang, Y., Yan, D., Wu, H., Cheng, J., Zhou, S., & Lui, J.C. (2016). Diversified temporal subgraph pattern mining. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.\u00a01965\u20131974.","DOI":"10.1145\/2939672.2939848"},{"key":"187_CR143","doi-asserted-by":"publisher","first-page":"49778","DOI":"10.1109\/ACCESS.2019.2911181","volume":"7","author":"X Sun","year":"2019","unstructured":"Sun, X., Tan, Y., Wu, Q., Chen, B., & Shen, C. (2019). Tm-miner: Tfs-based algorithm for mining temporal motifs in large temporal network. IEEE Access, 7, 49778\u201349789.","journal-title":"IEEE Access"},{"key":"187_CR144","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1007\/s11280-019-00674-0","volume":"23","author":"T Zhang","year":"2020","unstructured":"Zhang, T., Gao, Y., Qiu, L., Chen, L., Linghu, Q., & Pu, S. (2020). Distributed time-respecting flow graph pattern matching on temporal graphs. World Wide Web, 23, 609\u2013630.","journal-title":"World Wide Web"},{"issue":"3","key":"187_CR145","doi-asserted-by":"publisher","DOI":"10.1063\/1.5086059","volume":"29","author":"C Qu","year":"2019","unstructured":"Qu, C., Zhan, X., Wang, G., Wu, J., & Zhang, Z.-K. (2019). Temporal information gathering process for node ranking in time-varying networks. Chaos an Interdisciplinary Journal of Nonlinear Science, 29(3), Article 033116.","journal-title":"Chaos an Interdisciplinary Journal of Nonlinear Science"},{"key":"187_CR146","doi-asserted-by":"crossref","unstructured":"Singh, R.R. (2022). Centrality measures: a tool to identify key actors in social networks Principles of Social Networking: The New Horizon and Emerging Challenges, pp.\u00a01\u201327.","DOI":"10.1007\/978-981-16-3398-0_1"},{"issue":"1","key":"187_CR147","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/srep34944","volume":"6","author":"Y Shimada","year":"2016","unstructured":"Shimada, Y., Hirata, Y., Ikeguchi, T., & Aihara, K. (2016). Graph distance for complex networks. Scientific Reports, 6(1), 1\u20136.","journal-title":"Scientific Reports"},{"issue":"6","key":"187_CR148","doi-asserted-by":"publisher","DOI":"10.1098\/rsos.160196","volume":"3","author":"MJ Williams","year":"2016","unstructured":"Williams, M. J., & Musolesi, M. (2016). Spatio-temporal networks: reachability, centrality and robustness. Royal Society Open Science, 3(6), Article 160196.","journal-title":"Royal Society Open Science"},{"issue":"5","key":"187_CR149","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.98.052307","volume":"98","author":"J Petit","year":"2018","unstructured":"Petit, J., Gueuning, M., Carletti, T., Lauwens, B., & Lambiotte, R. (2018). Random walk on temporal networks with lasting edges. Physical Review E, 98(5), Article 052307.","journal-title":"Physical Review E"},{"issue":"1","key":"187_CR150","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-37534-2","volume":"9","author":"N Masuda","year":"2019","unstructured":"Masuda, N., & Holme, P. (2019). Detecting sequences of system states in temporal networks. Scientific Reports, 9(1), 1\u201311.","journal-title":"Scientific Reports"},{"key":"187_CR151","unstructured":"Huang, S., Cheng, J., & Wu, H. (2014). Temporal graph traversals: Definitions, algorithms, and applications. arXiv:1401.1919"},{"key":"187_CR152","doi-asserted-by":"crossref","unstructured":"Wehmuth, K., Ziviani, A., & Fleury, E. (2015). A unifying model for representing time-varying graphs. In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp.\u00a01\u201310, IEEE","DOI":"10.1109\/DSAA.2015.7344810"},{"issue":"4","key":"187_CR153","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.88.042804","volume":"88","author":"K Hoppe","year":"2013","unstructured":"Hoppe, K., & Rodgers, G. (2013). Mutual selection in time-varying networks. Physical Review E, 88(4), Article 042804.","journal-title":"Physical Review E"}],"container-title":["The Review of Socionetwork Strategies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12626-025-00187-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12626-025-00187-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12626-025-00187-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,29]],"date-time":"2025-10-29T03:04:41Z","timestamp":1761707081000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12626-025-00187-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,23]]},"references-count":153,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["187"],"URL":"https:\/\/doi.org\/10.1007\/s12626-025-00187-5","relation":{},"ISSN":["2523-3173","1867-3236"],"issn-type":[{"value":"2523-3173","type":"print"},{"value":"1867-3236","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,23]]},"assertion":[{"value":"13 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"On behalf of all authors, the corresponding author states that there is no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}