TY - STD TI - Kempe D, Kleinberg J, Tardos É. Maximizing the spread of influence through a social network. In: Proceedings of the Ninth ACM SIGKDD international conference on knowledge discovery and data mining-KDD ’03. p. 137–46. 2003. https://doi.org/10.1145/956750.956769. ID - ref1 ER - TY - STD TI - Habiba and Berger-Wolf TY. Maximizing the extent of spread in a dynamic network. DIMACS Technical Report. 2007. ID - ref2 ER - TY - STD TI - Murata T, Koga H. Methods for influence maximization in dynamic networks. In: Proceedings of the 6th international conference on complex networks and their applications (Complex Networks 2017), studies in computational intelligence. Berlin: Springer; 2017. p. 955–66. ID - ref3 ER - TY - JOUR AU - Babaei, M. AU - Mirzasoleiman, B. AU - Jalili, M. AU - Safari, M. A. PY - 2013 DA - 2013// TI - Revenue maximization in social networks through discounting JO - Soc Netw Anal Mining VL - 3 UR - https://doi.org/10.1007/s13278-012-0085-5 DO - 10.1007/s13278-012-0085-5 ID - Babaei2013 ER - TY - JOUR AU - Jalili, M. PY - 2013 DA - 2013// TI - Social power and opinion formation in complex network JO - Phys A VL - 392 UR - https://doi.org/10.1016/j.physa.2012.10.013 DO - 10.1016/j.physa.2012.10.013 ID - Jalili2013 ER - TY - JOUR AU - Jalili, M. PY - 2012 DA - 2012// TI - Effects of leaders and social power on opinion formation in complex networks JO - Simulation VL - 89 UR - https://doi.org/10.1177/0037549712462621 DO - 10.1177/0037549712462621 ID - Jalili2012 ER - TY - JOUR AU - Afshar, M. AU - Asadpour, M. PY - 2010 DA - 2010// TI - Opinion formation by informed agents JO - J Artif Soc Soc Simul VL - 13 UR - https://doi.org/10.18564/jasss.1665 DO - 10.18564/jasss.1665 ID - Afshar2010 ER - TY - JOUR AU - Garimella, K. AU - Morales, G. D. F. AU - Mathioudakis, M. AU - Gionis, A. PY - 2018 DA - 2018// TI - Polarization on social media JO - Web Conf 2018 Tutorial VL - 1 ID - Garimella2018 ER - TY - JOUR AU - Braha, D. AU - Bar-Yam, Y. PY - 2006 DA - 2006// TI - From centrality to temporary fame: dynamic centrality in complex networks JO - Complexity VL - 12 UR - https://doi.org/10.1002/cplx.20156 DO - 10.1002/cplx.20156 ID - Braha2006 ER - TY - STD TI - Braha D, Bar-Yam Y. Time-dependent complex networks: Dynamic centrality, dynamic motifs, and cycles of social interactions. In: Adaptive networks: theory, models and applications. 2009. p. 39–50. ID - ref10 ER - TY - JOUR AU - Hill, S. A. AU - Braha, D. PY - 2010 DA - 2010// TI - Dynamic model of time-dependent complex networks JO - Phys Rev E VL - 82 ID - Hill2010 ER - TY - JOUR AU - Holme, P. PY - 2015 DA - 2015// TI - Modern temporal network theory: a colloquium JO - Eur Phys J B VL - 88 ID - Holme2015 ER - TY - JOUR AU - Holme, P. AU - Saramäki, J. PY - 2012 DA - 2012// TI - Temporal networks JO - Phys Rep VL - 519 UR - https://doi.org/10.1016/j.physrep.2012.03.001.1108.1780 DO - 10.1016/j.physrep.2012.03.001.1108.1780 ID - Holme2012 ER - TY - JOUR AU - Jalili, M. AU - Perc, M. PY - 2017 DA - 2017// TI - Information dascades in complex networks JO - J Compl Netw VL - 5 ID - Jalili2017 ER - TY - STD TI - Chen W, Wang C, Wang Y. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining-KDD ’10. 2010. p. 1029–38. https://doi.org/10.1145/1835804.1835934. ID - ref15 ER - TY - STD TI - Jiang Q, Song G, Cong G, Wang Y, Si W, Xie K. Simulated annealing based influence maximization in social networks. In: Proceedings of the twenty-fifth AAAI conference on artificial intelligence. 2011. p. 127–132. ID - ref16 ER - TY - STD TI - Chen W, Wang Y, Yang S. Efficient influence maximization in social networks. In: Proceedings of the 15th ACM SIGKDD international conference on knowledge discovery and data mining-KDD ’09. 2009. p. 199–207. https://doi.org/10.1145/1557019.1557047.1204.4491. http://portal.acm.org/citation.cfm?doid=1557019.1557047. UR - http://portal.acm.org/citation.cfm?doid=1557019.1557047 ID - ref17 ER - TY - JOUR AU - Morone, F. AU - Makse, H. A. PY - 2015 DA - 2015// TI - Influence maximization in complex networks through optimal percolation JO - Nature VL - 524 UR - https://doi.org/10.1038/nature14604 DO - 10.1038/nature14604 ID - Morone2015 ER - TY - STD TI - Ohsaka N, Akiba T, Yoshida Y, Kawarabayashi K-i. Fast and accurate influence maximization on large networks with Pruned Monte-Carlo simulations. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence. 2014. p. 138–44. ID - ref19 ER - TY - STD TI - Borgs C, Brautbar M, Chayes J, Lucier B. Maximizing social influence in nearly optimal time. In: Proceedings of the twenty-fifth annual ACM-SIAM symposium on discrete algorithms. 2014. p. 946–57. ID - ref20 ER - TY - STD TI - Tang Y, Xiao X, Shi Y. Influence maximization: near-optimal time complexity meets practical efficiency. In: Proceedings of the 2014 ACM SIGMOD international conference on management of data. 2014. p. 75–86. ID - ref21 ER - TY - STD TI - Chen W, Lu W, Zhang N. Time-critical influence maximization in social networks with time-delayed diffusion process. In: Proceedings of the twenty-sixth AAAI conference on artificial intelligence. 2012. p. 592–8. http://www.aaai.org/ocs/index.php/AAAI/AAAI12/paper/viewFile/5024/5243 http://arxiv.org/abs/1204.3074. UR - http://arxiv.org/abs/1204.3074 ID - ref22 ER - TY - STD TI - Feng S, Chen X, Cong G, Yifeng Z, Yeow, Meng C, Yanping X. Influence maximization with novelty decay in social networks. In: Proceedings of the twenty-eighth AAAI conference on artificial intelligence. 2014. p. 37–43. ID - ref23 ER - TY - STD TI - Mihara S, Tsugawa S, Ohsaki H. Influence maximization problem for unknown social networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015-ASONAM ’15. 2015. p. 1539–46. https://doi.org/10.1145/2808797.2808885. ID - ref24 ER - TY - STD TI - Osawa S, Murata T. Selecting seed nodes for influence maximization in dynamic networks. In: Proceedings of the 6th workshop on complex networks (CompleNet 2015), studies in computational intelligence. Berlin: Springer; 2015. p. 91–8. ID - ref25 ER - TY - STD TI - Habiba, Yu Y, Berger-Wolf TY, Saia J. Finding spread blockers in dynamic networks. In: Advances in social network mining and analysis. 2010. vol. 5498, p. 55–76. ID - ref26 ER - TY - JOUR AU - Vanhems, P. AU - Barrat, A. AU - Cattuto, C. AU - Pinton, J. -. F. AU - Khanafer, N. AU - Régis, C. AU - Kim, B. -. A. AU - Comte, B. AU - Voirin, N. PY - 2013 DA - 2013// TI - Estimating potential infection transmission routes in hospital wards using wearable proximity sensors JO - PloS ONE VL - 8 UR - https://doi.org/10.1371/journal.pone.0073970 DO - 10.1371/journal.pone.0073970 ID - Vanhems2013 ER - TY - JOUR AU - Stehle, J. AU - Voirin, N. AU - Barrat, A. AU - Cattuto, C. AU - Isella, L. AU - Pinton, J. F. AU - Quaggiotto, M. AU - Broeck, W. AU - Regis, C. AU - Lina, B. AU - Vanhems, P. PY - 2011 DA - 2011// TI - High-resolution measurements of face-to-face contact patterns in a primary school JO - PloS ONE VL - 6 UR - https://doi.org/10.1371/journal.pone.0023176 DO - 10.1371/journal.pone.0023176 ID - Stehle2011 ER - TY - JOUR AU - Gemmetto, V. AU - Barrat, A. AU - Cattuto, C. PY - 2014 DA - 2014// TI - Mitigation of infectious disease at school: targeted class closure vs school closure JO - BMC Infect Dis VL - 14 UR - https://doi.org/10.1186/s12879-014-0695-9 DO - 10.1186/s12879-014-0695-9 ID - Gemmetto2014 ER - TY - JOUR AU - Mastrandrea, R. AU - Fournet, J. AU - Barrat, A. PY - 2015 DA - 2015// TI - Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys JO - PloS ONE VL - 10 UR - https://doi.org/10.1371/journal.pone.0136497 DO - 10.1371/journal.pone.0136497 ID - Mastrandrea2015 ER - TY - STD TI - Leskovec J, Krause A, Guestrin C, Faloutsos C, VanBriesen J, Glance N. Cost-effective outbreak detection in networks. Proceedings of the 13th ACM SIGKDD international conference on knowledge discovery and data mining-KDD ’07. 2007. 420–9. https://doi.org/10.1145/1281192.1281239. ID - ref31 ER -