Моделирование информационного противоборства в социальных сетях на основе теории игр и динамических байесовских сетей - page 12

С.В. Вельц
12
ЛИТЕРАТУРА
[1]
Khondker H. H. Role of the New Media in the Arab Spring.
Globalizations
,
2011, vol.8 (5), pp. 675‒679.
[2]
Kempe D., Kleinberg J., Tardos É. Maximizing the spread of influence
through a social network.
Proceeding KDD '03 Proceedings of the ninth ACM
SIGKDD international conference on Knowledge discovery and data mining,
2003, ACM New York, NY, USA, pp. 137‒146.
[3]
Domingos P., Richardson M. Mining the Network Value of Customers.
Pro-
ceedings of the Seventh International Conference on Knowledge Discovery
and Data Mining
, 2002.
[4]
Goldenberg J., Libai B., Muller E. Talk of the Network: A Complex Systems
Look at the Underlying Process of Word-of-Mouth.
Journal Marketing Let-
ters,
2001, vol. 12, Issue 3.
[5]
Kempe D., Kleinberg J., Tardos E. Influential nodes in a diffusion model for
social networks.
32nd International Colloquium on Automata, Languages and
Programming (ICALP)
, 2005, pp. 1127–1138.
[6]
Hill S., Provost F., Volinsky C. Network-based marketing: Identifying likely
adopters via consumer networks.
Journal of Computational and Graphical
Statistics
, 2006, 21(2), pp. 256―276.
[7]
U.S. Dept. of the Army and U.S. Marine Corps. The U.S. Army. Marine Corps
Counterinsurgency Field Manual. University of Chicago Press, 2007, pp. 3‒24.
[8]
Губанов Д.А., Новиков Д.А., Чхартишвили А.Г.
Социальные сети: моде-
ли информационного влияния, управления и противоборства
. Москва,
Физматлит, 2010, 228 с.
[9]
Рассел С., Норвиг П.
Искусственный интеллект: современный подход
.
Москва, Вильямс, 2006, 1406 с.
[10]
Leskovec J. Cost-effective outbreak detection in networks.
Proceedings of the
13th ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining (KDD)
, 2007, pp. 420‒429.
[11]
Zhang D., Gatica-perez D., Bengio S., Roy D. Learning influence among in-
teracting Markov chains.
Advances in Neural Information Processing Systems,
2005, 18.
[12]
Pelkowitz L. A continuous relaxation labeling algorithm for Markov random
fields.
IEEE Transactions on Systems, Man and Cybernetics
, 1990, vol. 20, pp.
709‒715.
[13]
Goyal A., Lu W., Lakshmanan L.V.S.
CELF++: Optimizing the greedy algo-
rithm for influence maximization in social networks
, 2011.
[14]
Chen W., Yuan Y., Zhang L.
Scalable influence maximization in social net-
works under the linear threshold model
. ICDM, 2010.
[15]
Goyal A. SIMPATH: An Efficient Algorithm for Influence Maximization un-
der the Linear Threshold Model.
Proceeding ICDM '11 Proceedings of the
2011 IEEE 11th International Conference on Data Mining,
2011.
[16]
Mathioudakis M., Bonchi F., Castillo C., Gionis A., Ukkonen A.
Sparsifica-
tion of influence networks
. KDD, 2011, pp. 529‒537.
[17]
Jiang Q. Song G., Cong G., Wang Y., Si W., Xie K.
Simulated Annealing
Based Influence Maximization in Social Networks
, AAAI, 2011.
[18]
He X., Song G., Chen W., Jiang Q. Influence blocking maximization in social
networks under the competitive linear threshold model.
Proceedings of the
12th SIAM International Conference on Data Mining (SDM'2012)
, 2012.
[19]
Chen W., Lu W., Zhang N. Time-critical influence maximization in social
networks with time-delayed diffusion process.
Proceedings of the 26th Con-
ference on Artificial Intelligence (AAAI'2012)
, 2012.
[20]
Tang J.
Social influence analysis in large-scale networks
, KDD, 2009.
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