P. Grabowicz, N. Ganguly, and K. Gummadi, Distinguishing between topical and non-topical information diffusion mechanisms in social media, ICWSM, pp.151-160, 2016.

X. He and D. Kempe, Robust influence maximization, SIGKDD, pp.885-894, 2016.
DOI : 10.1145/2939672.2939760

URL : http://dl.acm.org/ft_gateway.cfm?id=2939760&type=pdf

D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the spread of influence through a social network, SIGKDD, pp.137-146, 2003.

P. Lagrée, O. Cappé, B. Cautis, and S. Maniu, Effective large-scale online influence maximization, ICDM, 2017.

K. Lee, Influencer marketing 2.0: Key trends in 2017, 2017.

S. Lei, S. Maniu, L. Mo, R. Cheng, and P. Senellart, Online influence maximization, SIGKDD, 2015.

N. Levine, K. Crammer, and S. Mannor, Rotting bandits, NIPS, 2017.

J. Louëdec, L. Rossi, M. Chevalier, A. Garivier, and J. Mothe, Algorithme de bandit et obsolescence : un modèle pour la recommandation, 2016.

D. Mcallester and L. Ortiz, Concentration inequalities for the missing mass and for histogram rule error, JMLR, vol.4, pp.895-911, 2003.

D. Mcallester and R. Schapire, On the convergence rate of good-turing estimators, COLT, pp.1-6, 2000.

Q. Mei, J. Guo, and D. Radev, Divrank: The interplay of prestige and diversity in information networks, SIGKDD, 2010.

H. T. Nguyen, M. T. Thai, and T. N. Dinh, Stop-and-stare: Optimal sampling algorithms for viral marketing in billion-scale networks, SIGMOD, 2016.

N. Ohsaka, T. Akiba, Y. Yoshida, and K. Kawarabayashi, Fast and accurate influence maximization on large networks with pruned monte-carlo simulations, AAAI, 2014.

D. Romero, B. Meeder, and J. Kleinberg, Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter, WWW, pp.695-704, 2011.

Y. Rong, Q. Zhu, and H. Cheng, A model-free approach to infer the diffusion network from event cascade, CIKM, 2016.

K. Saito, R. Nakano, and M. Kimura, Prediction of information diffusion probabilities for independent cascade model, KES, pp.67-75, 2008.

C. Sletten, How to prepare brands for influencer fatigue, 2017.

Y. Tang, Y. Shi, and X. Xiao, Influence maximization in near-linear time: A martingale approach, SIGMOD, pp.1539-1554, 2015.

Y. Tang, X. Xiao, and Y. Shi, Influence maximization: Near-optimal time complexity meets practical efficiency, SIGMOD, pp.75-86, 2014.
DOI : 10.1145/3110025.3110041

S. Vaswani, B. Kveton, Z. Wen, M. Ghavamzadeh, L. Lakshmanan et al., Diffusion independent semi-bandit influence maximization, 2017.

S. Wang, X. Hu, P. Yu, and Z. Li, Mmrate: inferring multi-aspect diffusion networks with multi-pattern cascades, SIGKDD, pp.1246-1255, 2014.

D. Watts, Six Degrees: The Science of a Connected Age, 2003.

D. Watts and P. Dodds, Influentials, networks, and public opinion formation, Journal of Consumer Research, vol.34, issue.4, pp.441-458, 2007.

Z. Wen, B. Kveton, M. Valko, and S. Vaswani, Online influence maximization under independent cascade model with semi-bandit feedback, NIPS, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01643976