D. Kempe, J. Kleinberg, and ´. E. Tardos, Maximizing the spread of influence through a social network, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp.137-146, 2003.

A. Goyal, F. Bonchi, and L. V. Lakshmanan, A data-based approach to social influence maximization, Proceedings of the VLDB Endowment, vol.5, pp.73-84, 2011.

A. Vespignani, Modelling dynamical processes in complex socio-technical systems, Nature physics, vol.8, issue.1, p.32, 2012.

K. Saito, M. Kimura, K. Ohara, and H. Motoda, Learning continuous-time information diffusion model for social behavioral data analysis, Asian Conference on Machine Learning, pp.322-337, 2009.

A. Goyal, F. Bonchi, and L. V. Lakshmanan, Learning influence probabilities in social networks, Proceedings of the third ACM international conference on Web search and data mining, pp.241-250, 2010.

Y. Tang, Y. Shi, and X. Xiao, Influence maximization in near-linear time: A martingale approach, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp.1539-1554, 2015.

M. G. Rodriguez, D. Balduzzi, and B. Schölkopf, Uncovering the temporal dynamics of diffusion networks, 2011.

W. Chen, C. Wang, and Y. Wang, Scalable influence maximization for prevalent viral marketing in large-scale social networks, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.1029-1038, 2010.

A. Goyal, W. Lu, and L. V. Lakshmanan, Simpath: An efficient algorithm for influence maximization under the linear threshold model, IEEE 11th International Conference on, pp.211-220, 2011.

N. Du, L. Song, M. G. Rodriguez, and H. Zha, Scalable influence estimation in continuous-time diffusion networks, Advances in neural information processing systems, pp.3147-3155, 2013.

J. Leskovec, A. Krause, C. Guestrin, C. Faloutsos, J. Vanbriesen et al., Cost-effective outbreak detection in networks, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.420-429, 2007.

E. Cohen, D. Delling, T. Pajor, and R. F. Werneck, Sketch-based influence maximization and computation: Scaling up with guarantees, Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp.629-638, 2014.

Y. Tang, X. Xiao, and Y. Shi, Influence maximization: Near-optimal time complexity meets practical efficiency, Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp.75-86, 2014.

K. Saito, R. Nakano, and M. Kimura, Prediction of information diffusion probabilities for independent cascade model, International Conference on KnowledgeBased and Intelligent Information and Engineering Systems, pp.67-75, 2008.

M. G. Rodriguez and B. Schölkopf, Influence maximization in continuous time diffusion networks, 2012.

S. Bourigault, S. Lamprier, and P. Gallinari, Representation learning for information diffusion through social networks: an embedded cascade model, Proceedings of the Ninth ACM International Conference on Web Search and Data Mining, pp.573-582, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01316795

M. E. Rossi and M. Vazirgiannis, Exploring Network Centralities in Spreading Processes, International Symposium on Web AlGorithms (iSWAG), 2016.
URL : https://hal.archives-ouvertes.fr/hal-01346690

S. Pei, F. Morone, and H. A. Makse, Theories for influencer identification in complex networks, Complex Spreading Phenomena in Social Systems, pp.125-148, 2018.

L. K. Gallos, C. Song, and H. A. Makse, Scaling of degree correlations and its influence on diffusion in scale-free networks, Physical review letters, vol.100, issue.24, p.248701, 2008.

M. Kitsak, L. K. Gallos, S. Havlin, F. Liljeros, L. Muchnik et al., Identification of influential spreaders in complex networks, Nat Phys, vol.6, issue.11, pp.888-893, 2010.

J. Z. Qiu, J. Tang, H. Ma, Y. X. Dong, K. S. Wang et al., DeepInf: Modeling influence locality in large social networks, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'18), 2018.

C. Li, J. Ma, X. Guo, and Q. Mei, DeepCas: An end-to-end predictor of information cascades, Proceedings of the 26th International Conference on World Wide Web, pp.577-586, 2017.

F. D. Malliaros, M. E. Rossi, and M. Vazirgiannis, Locating influential nodes in complex networks, Scientific reports, vol.6, 2016.

G. Csardi and T. Nepusz, The igraph software package for complex network research, InterJournal, Complex Systems, issue.5, pp.1-9, 2006.

M. Xie, Q. Yang, Q. Wang, G. Cong, and G. De-melo, DynaDiffuse: A Dynamic Diffusion Model for Continuous Time Constrained Influence Maximization, AAAI, pp.346-352, 2015.

S. Jendoubi, A. Martin, L. Liétard, H. B. Hadji, and B. B. Yaghlane, Two evidential data based models for influence maximization in twitter. Knowledge-Based Systems, vol.121, pp.58-70, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01435733

J. Zhang, B. Liu, J. Tang, T. Chen, and J. Li, Social Influence Locality for Modeling Retweeting Behaviors, In IJCAI, vol.13, pp.2761-2767, 2013.

M. Cha, H. Haddadi, F. Benevenuto, and P. K. Gummadi, Measuring user influence in twitter: The million follower fallacy, Icwsm, vol.10, p.30, 2010.