Y. Abbasi-yadkori, D. Pál, and C. Szepesvári, Improved algorithms for linear stochastic bandits, Neural Information Processing Systems, 2011.

Y. Bao, X. Wang, Z. Wang, C. Wu, and F. C. Lau, Online influence maximization in non-stationary Social Networks, 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), 2016.
DOI : 10.1109/IWQoS.2016.7590438

N. Barbieri, F. Bonchi, and G. Manco, Topic-aware social influence propagation models. Knowledge and information systems, pp.555-584, 2013.
DOI : 10.1109/icdm.2012.122

Z. Bnaya, R. Puzis, R. Stern, and A. Felner, Social network search as a volatile multi-armed bandit problem, Human Journal, vol.2, issue.2, pp.84-98, 2013.

A. Carpentier and M. Valko, Revealing graph bandits for maximizing local influence, International Conference on Artificial Intelligence and Statistics, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01304020

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, KDD '10, 2010.
DOI : 10.1145/1835804.1835934

W. Chen, Y. Wang, and Y. Yuan, Combinatorial multi-armed bandit: General framework, results and applications, International Conference on Machine Learning, 2013.

W. Chen, Y. Wang, and Y. Yuan, Combinatorial multi-armed bandit and its extension to probabilistically triggered arms, Journal of Machine Learning Research, vol.17, 2016.

V. Dani, P. Thomas, . Hayes, M. Sham, and . Kakade, Stochastic linear optimization under bandit feedback, Conference on Learning Theory, 2008.

D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, 2010.
DOI : 10.1017/CBO9780511761942

M. Fang and D. Tao, Networked bandits with disjoint linear payoffs, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '14, 2014.
DOI : 10.1145/2623330.2623672

M. Farajtabar, X. Ye, S. Harati, L. Song, and H. Zha, Multistage campaigning in social networks, Neural Information Processing Systems, 2016.

M. Gomez-rodriguez, . Schölkopf, J. Langford, and . Pineau, Influence Estimation and Maximization in Continuous-Time Diffusion Networks, International Conference on Machine Learning, 2012.
DOI : 10.1093/aje/kwh255

A. Goyal, F. Bonchi, V. Laks, and . Lakshmanan, Learning influence probabilities in social networks, Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pp.241-250, 2010.
DOI : 10.1145/1718487.1718518

URL : http://www-kdd.isti.cnr.it/~bonchi/wsdm339-goyal.pdf

A. Grover and J. Leskovec, node2vec, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, 2016.
DOI : 10.1137/1.9781611974010.51

URL : https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5108654/pdf

D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the spread of influence through a social network. Knowledge Discovery and Data Mining, p.137, 2003.

C. Branislav-kveton, Z. Szepesvari, A. Wen, and . Ashkan, Cascading bandits: Learning to rank in the cascade model, Proceedings of the 32nd International Conference on Machine Learning, 2015.

Z. Branislav-kveton and . Wen, Azin Ashkan, and Csaba Szepesvari. Combinatorial cascading bandits, Advances in Neural Information Processing Systems 28, pp.1450-1458, 2015.

Z. Branislav-kveton and . Wen, Azin Ashkan, and Csaba Szepesvari. Tight regret bounds for stochastic combinatorial semi-bandits, Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 2015.

P. Lagrée, O. Cappé, B. Cautis, and S. Maniu, Effective large-scale online influence maximization, International Conference on Data Mining, 2017.

S. Lei, S. Maniu, L. Mo, R. Cheng, and P. Senellart, Online influence maximization, Knowledge Discovery and Data mining, 2015.
DOI : 10.1145/2783258.2783271

J. Leskovec and A. Krevl, Snap datasets: Stanford large network dataset collection, 2014.

Y. Li, W. Chen, Y. Wang, and Z. Zhang, Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships, Proceedings of the sixth ACM international conference on Web search and data mining, WSDM '13, 2013.
DOI : 10.1145/2433396.2433478

P. Netrapalli and S. Sanghavi, Learning the graph of epidemic cascades, ACM SIGMETRICS Performance Evaluation Review, vol.40, issue.1, pp.211-222, 2012.
DOI : 10.1145/2318857.2254783

K. Saito, R. Nakano, and M. Kimura, Prediction of Information Diffusion Probabilities for Independent Cascade Model, Knowledge-Based Intelligent Information and Engineering Systems, pp.67-75, 2008.
DOI : 10.1007/978-3-540-85567-5_9

A. Singla, E. Horvitz, P. Kohli, R. White, and A. Krause, Information gathering in networks via active exploration, International Joint Conferences on Artificial Intelligence, 2015.

Y. Tang, X. Xiao, and S. Yanchen, Influence maximization, Proceedings of the 2014 ACM SIGMOD international conference on Management of data, SIGMOD '14, 2014.
DOI : 10.1145/2588555.2593670

M. Valko, Bandits on graphs and structures. habilitation, École normale supérieure de Cachan, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01359757

S. Vaswani, B. Kveton, Z. Wen, M. Ghavamzadeh, V. Laks et al., Model-independent online learning for influence maximization, International Conference on Machine Learning, 2017.

S. Vaswani, V. S. Laks, and . Lakshmanan, Adaptive influence maximization in social networks: Why commit when you can adapt?, 2016.

S. Vaswani, L. V. Lakshmanan, and M. Schmidt, Influence maximization with bandits, NIPS workshop on Networks in the Social and Information Sciences 2015, 2015.

Q. Wang and W. Chen, Improving regret bounds for combinatorial semi-bandits with probabilistically triggered arms and its applications, Neural Information Processing Systems, 2017.

Z. Wen, B. Kveton, and A. Ashkan, Efficient learning in large-scale combinatorial semi-bandits, International Conference on Machine Learning, 2015.

S. Zong, H. Ni, K. Sung, N. R. Ke, Z. Wen et al., Cascading bandits for large-scale recommendation problems, Uncertainty in Artificial Intelligence, 2016.