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We consider the problem of selecting a seed set to maximize the expectednumber of influenced nodes in the social network, referred to as the\textit{influence maximization} (IM) problem. We assume that the topology ofthe social network is prescribed while the influence probabilities among edgesare unknown. In order to learn the influence probabilities and simultaneouslymaximize the influence spread, we consider the tradeoff between exploiting thecurrent estimation of the influence probabilities to ensure certain influencespread and exploring more nodes to learn better about the influenceprobabilities. The exploitation-exploration trade-off is the core issue in themulti-armed bandit (MAB) problem. If we regard the influence spread as thereward, then the IM problem could be reduced to the combinatorial multi-armedbandits. At each round, the learner selects a limited number of seed nodes inthe social network, then the influence spreads over the network according tothe real influence probabilities. The learner could observe the activationstatus of the edge if and only if its start node is influenced, which isreferred to as the edge-level semi-bandit feedback. Two classical banditalgorithms including Thompson Sampling and Epsilon Greedy are used to solvethis combinatorial problem. To ensure the robustness of these two algorithms,we use an automatic ensemble learning strategy, which combines the explorationstrategy with exploitation strategy. The ensemble algorithm is self-adaptiveregarding that the probability of each algorithm could be adjusted based on thehistorical performance of the algorithm. Experimental evaluation illustratesthe effectiveness of the automatically adjusted hybridization of explorationalgorithm with exploitation algorithm.

Automatic Ensemble Learning for Online Influence Maximization