A. Arora, S. Galhotra, and S. Ranu, Debunking the Myths of Innuence Maximization: An In-Depth Benchmarking Study, SIGMOD, 2017.

N. Barbieri, F. Bonchi, and G. Manco, Topic-aware social influence propagation models, Knowledge and Information Systems, vol.30, issue.3, pp.555-584, 2013.
DOI : 10.1007/s10115-013-0646-6

D. Berend and A. Kontorovich, On the concentration of the missing mass, Electronic Communications in Probability, pp.1-7, 2013.
DOI : 10.1214/ECP.v18-2359

S. Boucheron, G. Lugosi, P. Massart, and M. Ledoux, Concentration inequalities : a nonasymptotic theory of independence, 2013.
DOI : 10.1093/acprof:oso/9780199535255.001.0001
URL : https://hal.archives-ouvertes.fr/hal-00794821

S. Bubeck and N. Cesa-bianchi, Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Foundations and Trends?? in Machine Learning, vol.5, issue.1, pp.1-122, 2012.
DOI : 10.1561/2200000024

S. Bubeck, D. Ernst, and A. Garivier, Optimal discovery with probabilistic expert advice, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pp.601-623, 2013.
DOI : 10.1109/CDC.2012.6426724
URL : https://hal.archives-ouvertes.fr/hal-00811860

W. Chen, T. Lin, Z. Tan, M. Zhao, and X. Zhou, Robust Innuence Maximization, SIGKDD, pp.795-804, 2016.

W. Chen, Y. Wang, Y. Yuan, and Q. Wang, Combinatorial Multi-armed Bandit and Its Extension to Probabilistically Triggered Arms, pp.1746-1778, 2016.

N. Du, L. Song, H. Woo, and H. Zha, Uncover Topic-Sensitive Information Diiusion Networks, AISTATS, pp.229-237, 2013.

D. Easley and J. Kleinberg, Networks, Crowds, and Markets -Reasoning About a Highly Connected World, 2010.

M. Gomez-rodriguez, D. Balduzzi, and B. Schölkopf, Uncovering the Temporal Dynamics of Diiusion Networks, ICML, pp.561-568, 2011.

M. Gomez-rodriguez, J. Leskovec, and A. Krause, Inferring Networks of Diffusion and Influence, ACM Transactions on Knowledge Discovery from Data, vol.5, issue.4, pp.1-2137, 2012.
DOI : 10.1145/2086737.2086741

M. Gomez-rodriguez, J. Leskovec, and B. Schölkopf, Structure and dynamics of information pathways in online media, Proceedings of the sixth ACM international conference on Web search and data mining, WSDM '13, pp.23-32, 2013.
DOI : 10.1145/2433396.2433402

I. J. Good, e Population Frequencies of Species and the Estimation of Population Parameters, Biometrika, vol.40, pp.3-4, 1953.

A. Goyal, F. Bonchi, and L. Lakshmanan, Learning innuence probabilities in social networks, WSDM, pp.241-250, 2010.

P. Grabowicz, N. Ganguly, and K. Gummadi, Distinguishing between Topical and Non-Topical Information Diiusion Mechanisms in Social Media, ICWSM, pp.151-160, 2016.

X. He and D. Kempe, Robust Innuence Maximization, SIGKDD, pp.885-894, 2016.

D. Kempe and J. Kleinberg, Maximizing the Spread of Innuence rough a Social Network, SIGKDD. ACM, pp.137-146, 2003.

S. Lei, S. Maniu, L. Mo, R. Cheng, and P. Senellart, Online Innuence Maximization, SIGKDD, 2015.

D. Mcallester and L. Ortiz, Concentration Inequalities for the Missing Mass and for Histogram Rule Error, 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, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, 2010.
DOI : 10.1145/1835804.1835931

H. T. Nguyen, M. T. , and T. N. Dinh, Stop-and-Stare, Proceedings of the 2016 International Conference on Management of Data, SIGMOD '16, 2016.
DOI : 10.1145/2882903.2915207

N. Ohsaka, T. Akiba, Y. Yoshida, and K. Kawarabayashi, Fast and Accurate Innuence Maximization on Large Networks with Pruned Monte-Carlo Simulations, AAAI, 2014.

D. Romero, B. Meeder, and J. Kleinberg, Diierences in the Mechanics of Information Diiusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twiier, WWW, pp.695-704, 2011.

Y. Rong, Q. Zhu, and H. Cheng, A Model-Free Approach to Infer the Diiusion Network from Event Cascade, CIKM, 2016.

K. Saito, R. Nakano, and M. Kimura, Prediction of Information Diiusion Probabilities for Independent Cascade Model, KES, pp.67-75, 2008.

Y. Tang, Y. Shi, and X. Xiao, Innuence Maximization in Near-Linear Time: A Martingale Approach, SIGMOD, pp.1539-1554, 2015.

Y. Tang, X. Xiao, and Y. Shi, Innuence Maximization: Near-Optimal Time Complexity Meets Practical EEciency, SIGMOD, pp.75-86, 2014.
DOI : 10.1145/2588555.2593670

S. Vaswani, V. S. Lakshmanan, and M. Schmidt, Innuence Maximization with Bandits, Workshop NIPS (NIPS '15), 2015.

S. Wang, X. Hu, P. Yu, and Z. Li, MMRate, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '14, pp.1246-1255, 2014.
DOI : 10.1145/2623330.2623728

D. Waas, Six Degrees: e Science of a Connected Age, 2003.

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

Z. Wen, B. Kveton, and M. Valko, Innuence Maximization with Semi-Bandit Feedback, 2016.