  Influence maximization is the task of finding the smallest set of nodes whose
activation in a social network can trigger an activation cascade that reaches
the targeted network coverage, where threshold rules determine the outcome of
influence. This problem is NP-hard and it has generated a significant amount of
recent research on finding efficient heuristics. We focus on a {\it Balance
Index} algorithm that relies on three parameters to tune its performance to the
given network structure. We propose using a supervised machine-learning
approach for such tuning. We select the most influential graph features for the
parameter tuning. Then, using random-walk-based graph-sampling, we create small
snapshots from the given synthetic and large-scale real-world networks. Using
exhaustive search, we find for these snapshots the high accuracy values of BI
parameters to use as a ground truth. Then, we train our machine-learning model
on the snapshots and apply this model to the real-word network to find the best
BI parameters. We apply these parameters to the sampled real-world network to
measure the quality of the sets of initiators found this way. We use various
real-world networks to validate our approach against other heuristic.
