@conference {1504015,
	title = {Bayesian security games for controlling contagion },
	booktitle = {ASE/IEEE International Conference on Social Computing(SocialCom)},
	year = {2013},
	abstract = {Influence blocking games have been used to model
adversarial domains with a social component, such as counterinsurgency. In these games, a mitigator attempts to minimize the
efforts of an influencer to spread his agenda across a social
network. Previous work has assumed that the influence graph
structure is known with certainty by both players. However, in
reality, there is often significant information asymmetry between
the mitigator and the influencer. We introduce a model of this
information asymmetry as a two-player zero-sum Bayesian game.
Nearly all past work in influence maximization and social network
analysis suggests that graph structure is fundamental in strategy
generation, leading to an expectation that solving the Bayesian
game exactly is crucial. Surprisingly, we show through extensive
experimentation on synthetic and real-world social networks that
many common forms of uncertainty can be addressed nearoptimally by ignoring the vast majority of it and simply solving an
abstracted game with a few randomly chosen types. This suggests
that optimal strategies of games that do not model the full range
of uncertainty in influence blocking games are typically robust
to uncertainty about the influence graph structure.},
	author = {J. Tsai and Y. Qian and Y. Vorobeychik and C. Kiekintveld and M. Tambe}
}
