D. Kempe and J. Kleinberg, Maximizing the spread of influence through a social network, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.137-146, 2003.
DOI : 10.1145/956750.956769

A. Goyal, W. Lu, and L. V. Lakshmanan, CELF++, Proceedings of the 20th international conference companion on World wide web, WWW '11, pp.47-48, 2011.
DOI : 10.1145/1963192.1963217

W. Chen, Y. Wang, and S. Yang, Efficient influence maximization in social networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.199-208, 2009.
DOI : 10.1145/1557019.1557047

C. Wang, W. Chen, and Y. Wang, Scalable influence maximization for independent cascade model in large-scale social networks, Data Mining and Knowledge Discovery, vol.8, issue.3, p.545, 2012.
DOI : 10.1137/0208032

K. Jung, W. Heo, and W. Chen, IRIE: Scalable and Robust Influence Maximization in Social Networks, 2012 IEEE 12th International Conference on Data Mining, pp.918-923, 2012.
DOI : 10.1109/ICDM.2012.79

URL : http://arxiv.org/pdf/1111.4795

D. Golovin and A. Krause, Adaptive submodularity: A new approach to active learning and stochastic optimization, COLT, pp.333-345, 2010.

A. Badanidiyuru, C. Papadimitriou, A. Rubinstein, L. Seeman, and Y. Singer, Locally Adaptive Optimization: Adaptive Seeding for Monotone Submodular Functions, Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms, pp.414-429, 2016.
DOI : 10.1137/1.9781611974331.ch31

URL : http://epubs.siam.org/doi/pdf/10.1137/1.9781611974331.ch31

Y. Singer, Influence maximization through adaptive seeding, ACM SIGecom Exchanges, vol.15, issue.1, pp.32-59, 2016.
DOI : 10.1145/1993636.1993740

L. Seeman and Y. Singer, Adaptive Seeding in Social Networks, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, pp.459-468, 2013.
DOI : 10.1109/FOCS.2013.56

A. Rubinstein, L. Seeman, and Y. Singer, Approximability of Adaptive Seeding under Knapsack Constraints, Proceedings of the Sixteenth ACM Conference on Economics and Computation, EC '15, pp.797-814, 2015.
DOI : 10.1016/S0167-6377(03)00062-2

URL : http://www.cs.cornell.edu/%7Elseeman/Docs/AdaptiveSeedingKnapsack.pdf

T. Horel and Y. Singer, Scalable methods for adaptively seeding a social network, WWW, 2015, pp.441-451
DOI : 10.1145/2740908.2744108

URL : http://arxiv.org/pdf/1503.01438

J. Correa, M. Kiwi, N. Olver, and A. Vera, Adaptive Rumor Spreading, WINE, vol.42, issue.6, pp.272-285, 2015.
DOI : 10.1109/FOCS.2013.56

URL : https://ir.cwi.nl/pub/26956/10.1007_978-3-662-48995-6_20.pdf

S. Dhamal, K. J. Prabuchandran, and Y. Narahari, Information Diffusion in Social Networks in Two Phases, IEEE Transactions on Network Science and Engineering, vol.3, issue.4, pp.197-210, 2016.
DOI : 10.1109/TNSE.2016.2610838

G. Tong, W. Wu, S. Tang, and D. Du, Adaptive Influence Maximization in Dynamic Social Networks, IEEE/ACM Transactions on Networking, vol.25, issue.1, pp.112-125, 2016.
DOI : 10.1109/TNET.2016.2563397

URL : http://arxiv.org/pdf/1506.06294

J. Yuan and S. Tang, No Time to Observe: Adaptive Influence Maximization with Partial Feedback, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, pp.3908-3914, 2017.
DOI : 10.24963/ijcai.2017/546

URL : https://www.ijcai.org/proceedings/2017/0546.pdf

S. Mondal, S. Dhamal, and Y. Narahari, Two-phase influence maximization in social networks with seed nodes and referral incentives, in ICWSM, pp.620-623, 2017.

J. Leskovec and J. J. Mcauley, Learning to discover social circles in ego networks, Advances in neural information processing systems, pp.539-547, 2012.

Z. Zhang, H. Wu, K. Yue, J. Li, and W. Liu, Influence Maximization for Cascade Model with Diffusion Decay in Social Networks, ICYCSEE, vol.26, issue.8, pp.418-427, 2016.
DOI : 10.1017/CBO9780511815478