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 which is modeled as a
graph. 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
would be vastly superior to any technique that does not account
for uncertainty about the network structure. Surprisingly, we show
through extensive experimentation on synthetic and real-world social networks that many common forms of uncertainty can be addressed near-optimally 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 in
many cases robust to uncertainty about the structure of the influence graph.