  We consider the problem of selecting a seed set to maximize the expected
number of influenced nodes in the social network, referred to as the
\textit{influence maximization} (IM) problem. We assume that the topology of
the social network is prescribed while the influence probabilities among edges
are unknown. In order to learn the influence probabilities and simultaneously
maximize the influence spread, we consider the tradeoff between exploiting the
current estimation of the influence probabilities to ensure certain influence
spread and exploring more nodes to learn better about the influence
probabilities. The exploitation-exploration trade-off is the core issue in the
multi-armed bandit (MAB) problem. If we regard the influence spread as the
reward, then the IM problem could be reduced to the combinatorial multi-armed
bandits. At each round, the learner selects a limited number of seed nodes in
the social network, then the influence spreads over the network according to
the real influence probabilities. The learner could observe the activation
status of the edge if and only if its start node is influenced, which is
referred to as the edge-level semi-bandit feedback. Two classical bandit
algorithms including Thompson Sampling and Epsilon Greedy are used to solve
this combinatorial problem. To ensure the robustness of these two algorithms,
we use an automatic ensemble learning strategy, which combines the exploration
strategy with exploitation strategy. The ensemble algorithm is self-adaptive
regarding that the probability of each algorithm could be adjusted based on the
historical performance of the algorithm. Experimental evaluation illustrates
the effectiveness of the automatically adjusted hybridization of exploration
algorithm with exploitation algorithm.
