S. Dhamal, W. Ben-ameur, T. Chahed, and E. Altman, Manipulating opinion dynamics in social networks in two phases, The Joint International Workshop on Social Influence Analysis and Mining Actionable Insights from Social Networks, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01800311

D. Easley and J. Kleinberg, Networks, crowds, and markets, 2010.
DOI : 10.1017/cbo9780511761942

D. Acemoglu and A. Ozdaglar, Opinion dynamics and learning in social networks, Dynamic Games and Applications, vol.1, issue.1, pp.3-49, 2011.
DOI : 10.1007/s13235-010-0004-1

A. Gionis, E. Terzi, and P. Tsaparas, Opinion maximization in social networks, 2013 International Conference on Data Mining, pp.387-395, 2013.
DOI : 10.1137/1.9781611972832.43

URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972832.43

M. Grabisch, A. Mandel, A. Rusinowska, and E. Tanimura, Strategic influence in social networks, vol.43, pp.29-50, 2018.
DOI : 10.1287/moor.2017.0853

URL : https://hal.archives-ouvertes.fr/hal-01158168

S. Dhamal, W. Ben-ameur, T. Chahed, and E. Altman, Optimal investment strategies for competing camps in a social network: A broad framework, IEEE Transactions 895 on Network Science and Engineering, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01712288

N. Friedkin and E. Johnsen, Social influence and opinions, Journal of Mathematical Sociology, vol.15, issue.3-4, pp.193-206, 1990.
DOI : 10.1080/0022250x.1990.9990069

N. Friedkin and E. Johnsen, Social positions in influence networks, Social Networks, vol.19, issue.3, pp.209-222, 1997.
DOI : 10.1017/cbo9780511976735.018

U. Krause, A discrete nonlinear and non-autonomous model of consensus formation, Communications in Difference Equations, pp.227-236, 2000.

H. Xia, H. Wang, and Z. Xuan, Opinion dynamics: A multidisciplinary review and perspective on future research, Multidisciplinary Studies in Knowledge and Systems Science, pp.311-332, 2013.

D. Kempe, J. Kleinberg, and É. Tardos, Maximizing the spread of influence through a social network, 9th International Conference on Knowledge Discovery and Data Mining, pp.137-146, 2003.

A. Guille, H. Hacid, C. Favre, and D. A. Zighed, Information diffusion in online social networks: A survey, ACM SIGMOD Record, vol.42, issue.1, pp.17-28, 2013.
DOI : 10.1145/2503792.2503797

URL : https://hal.archives-ouvertes.fr/hal-00848050

M. Gomez-rodriguez, L. Song, N. Du, H. Zha, and B. Schölkopf, Influence estimation and maximization in continuous-time diffusion networks, ACM Transactions on Information Systems (TOIS), vol.34, issue.2, p.33, 2016.

M. Farajtabar, Y. Wang, M. Gomez-rodriguez, S. Li, and H. Zha, COEVOLVE: A joint point process model for information diffusion and network evolution, Journal 915 of Machine Learning Research, vol.18, pp.1-49, 2017.

J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, International Journal of Modern Physics C, vol.18, issue.12, pp.1819-1838, 2007.

M. Gomez-rodriguez and L. Song, Diffusion in social and information networks: Research problems, probabilistic models and machine learning methods, Proceed-920 ings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.2315-2316, 2015.

M. H. Degroot, Reaching a consensus, Journal of the American Statistical Association, vol.69, issue.345, pp.118-121, 1974.

R. Holley and T. Liggett, Ergodic theorems for weakly interacting infinite systems 925 and the voter model, The Annals of Probability, pp.643-663, 1975.
DOI : 10.1214/aop/1176996306

URL : https://doi.org/10.1214/aop/1176996306

E. Yildiz, A. Ozdaglar, D. Acemoglu, A. Saberi, and A. Scaglione, Binary opinion dynamics with stubborn agents, ACM Transactions on Economics and Computation, vol.1, issue.4, p.30, 2013.
DOI : 10.1145/2538508

C. Lynn and D. D. Lee, Maximizing influence in an Ising network: A mean-field 930 optimal solution, Advances in Neural Information Processing Systems, pp.2495-2503, 2016.

R. A. Rossi and N. K. Ahmed, Role discovery in networks, IEEE Transactions on Knowledge and Data Engineering, vol.27, issue.4, pp.1112-1131, 2015.
DOI : 10.1109/tkde.2014.2349913

URL : http://arxiv.org/pdf/1405.7134.pdf

S. Abiteboul, M. Preda, and G. Cobena, Adaptive on-line page importance computa-935 tion, Proceedings of the 12th international conference on World Wide Web, pp.280-290, 2003.
DOI : 10.1145/775189.775192

P. Grindrod, M. C. Parsons, D. J. Higham, and E. Estrada, Communicability across evolving networks, Physical Review E, vol.83, issue.4, p.46120, 2011.
DOI : 10.1103/physreve.83.046120

URL : http://www.reading.ac.uk/web/FILES/maths/preprint_10_32_Grindrod.pdf

D. F. Gleich and R. A. Rossi, A dynamical system for pagerank with time-dependent 940 teleportation, vol.10, pp.188-217, 2014.
DOI : 10.1080/15427951.2013.814092

URL : http://arxiv.org/pdf/1211.4266.pdf

S. A. Myers and J. Leskovec, Clash of the contagions: Cooperation and competition in information diffusion, Proceedings of the Twelfth International Conference on Data Mining (ICDM), pp.539-548, 2012.

V. Tzoumas, C. Amanatidis, and E. Markakis, A game-theoretic analysis of a com-945 petitive diffusion process over social networks, International Workshop on Internet and Network Economics, pp.1-14, 2012.

S. R. Etesami and T. Ba?ar, Complexity of equilibrium in competitive diffusion games on social networks, Automatica, vol.68, pp.100-110, 2016.

S. Bharathi, D. Kempe, and M. Salek, Competitive influence maximization in so-950 cial networks, 3rd International Workshop on Web and Internet Economics, pp.306-311, 2007.
DOI : 10.1007/978-3-540-77105-0_31

URL : http://www-rcf.usc.edu/~dkempe/publications/viral-competitive.pdf

S. Goyal, H. Heidari, and M. Kearns, Competitive contagion in networks, Games and Economic Behavior
DOI : 10.2139/ssrn.1950644

URL : http://www.cis.upenn.edu/%7Emkearns/papers/GoyalKearnsSTOCFinal.pdf

A. Anagnostopoulos, D. Ferraioli, and S. Leonardi, Competitive influence in social 955 networks: Convergence, submodularity, and competition effects, 14th International Conference on Autonomous Agents & Multiagent Systems, pp.1767-1768, 2015.

J. Ghaderi and R. Srikant, Opinion dynamics in social networks with stubborn agents: Equilibrium and convergence rate, Automatica, vol.50, issue.12, pp.3209-3215, 2014.
DOI : 10.1016/j.automatica.2014.10.034

P. Dubey, R. Garg, and B. D. Meyer, Competing for customers in a social network: The quasi-linear case, 2nd International Workshop on Web and Internet Economics, pp.162-173, 2006.
URL : https://hal.archives-ouvertes.fr/hal-00367866

K. Bimpikis, A. Ozdaglar, and E. Yildiz, Competitive targeted advertising over networks, Operations Research, vol.64, issue.3, pp.705-720, 2016.
DOI : 10.1287/opre.2015.1430

Y. Singer, Influence maximization through adaptive seeding, ACM SIGecom Exchanges, vol.15, issue.1, pp.32-59, 2016.
DOI : 10.1145/2994501.2994503

D. Golovin and A. Krause, Adaptive submodularity: theory and applications in active learning and stochastic optimization, Journal of Artificial Intelligence Research, vol.42, issue.1, pp.427-486, 2011.

L. Seeman and Y. Singer, Adaptive seeding in social networks, 54th Annual IEEE Symposium on Foundations of Computer Science, pp.459-468, 2013.
DOI : 10.1109/focs.2013.56

URL : http://www.cs.cornell.edu/~lseeman/Docs/AdaptiveSeedingFocs.pdf

A. Rubinstein, L. Seeman, and Y. Singer, Approximability of adaptive seeding under knapsack constraints, 16th ACM Conference on Economics and Computation, pp.797-814, 2015.
DOI : 10.1145/2764468.2764512

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

T. Horel and Y. Singer, Scalable methods for adaptively seeding a social network, 24th International Conference on World Wide Web, pp.441-451, 2015.
DOI : 10.1145/2740908.2744108

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

J. Correa, M. Kiwi, N. Olver, and A. Vera, Adaptive rumor spreading, 11th International Conference on Web and Internet Economics, pp.272-285, 2015.
DOI : 10.1007/978-3-662-48995-6_20

URL : https://doi.org/10.1007/978-3-662-48995-6_20

A. Badanidiyuru, C. Papadimitriou, A. Rubinstein, L. Seeman, and Y. Singer, Locally 980 adaptive optimization: Adaptive seeding for monotone submodular functions, 27th ACM-SIAM Symposium on Discrete Algorithms, SIAM, pp.414-429, 2016.
DOI : 10.1137/1.9781611974331.ch31

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

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.

S. , Effectiveness of diffusing information through a social network in multiple phases, IEEE Global Communications Conference, pp.1-7, 2018.

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, 26th 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

L. Sun, W. Huang, P. S. Yu, and W. Chen, Multi-round influence maximization, Proceedings of the 24th ACM SIGKDD International Conference on Knowledge 995
DOI : 10.1145/3219819.3220101

, Discovery & Data Mining, pp.2249-2258, 2018.

S. Mondal, S. Dhamal, and Y. Narahari, Two-phase influence maximization in social networks with seed nodes and referral incentives, International AAAI Conference on Web and Social Media, pp.620-623, 2017.

S. Dhamal, W. Ben-ameur, T. Chahed, and E. Altman, Optimal multiphase invest-1000 ment strategies for influencing opinions in a social network, 17th International Conference on Autonomous Agents & Multiagent Systems, pp.1927-1929, 2018.

C. Altafini, Consensus problems on networks with antagonistic interactions, IEEE Transactions on Automatic Control, vol.58, issue.4, pp.935-946, 2013.
DOI : 10.1109/tac.2012.2224251

A. V. Proskurnikov, A. S. Matveev, and M. Cao, Opinion dynamics in social networks with hostile camps: Consensus vs. polarization, IEEE Transactions on Automatic Control, vol.61, issue.6, pp.1524-1536, 2016.
DOI : 10.1109/tac.2015.2471655

URL : http://arxiv.org/pdf/1508.05034

L. Katz, A new status index derived from sociometric analysis, Psychometrika, vol.18, issue.1, pp.39-43, 1953.
DOI : 10.1007/bf02289026

S. Boyd and L. Vandenberghe, Convex optimization, 2004.

K. J. Arrow, L. Hurwicz, H. Uzawa, and H. B. Chenery, Studies in linear and nonlinear programming, 1958.

M. Osborne, An introduction to game theory, vol.3, p.1015

W. Chen, Y. Wang, and S. Yang, Efficient influence maximization in social networks, 15th International Conference on Knowledge Discovery and Data Mining, pp.199-208, 2009.
DOI : 10.1145/1557019.1557047

W. Chen, C. Wang, and Y. Wang, Scalable influence maximization for prevalent viral 1020 marketing in large-scale social networks, 16th International Conference on Knowledge Discovery and Data Mining, pp.1029-1038, 2010.
DOI : 10.1145/1835804.1835934

W. W. Zachary, An information flow model for conflict and fission in small groups, Journal of Anthropological Research, vol.33, issue.4, pp.452-473, 1977.
DOI : 10.1086/jar.33.4.3629752

URL : http://arxiv.org/pdf/1707.03587

S. , An integrated framework for competitive multi-channel marketing of 1025 multi-featured products, Proceedings of the 11th International Conference on Communication Systems & Networks, pp.391-394, 2019.

T. H. Haveliwala, Topic-sensitive pagerank, Proceedings of the 11th international conference on World Wide Web, pp.517-526, 2002.
DOI : 10.1145/511446.511513

G. Jeh and J. Widom, Scaling personalized web search, Proceedings of the 12th 1030 international conference on World Wide Web, pp.271-279, 2003.
DOI : 10.1145/775152.775191