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Influence maximization is the task of finding the smallest set of nodes whoseactivation in a social network can trigger an activation cascade that reachesthe targeted network coverage, where threshold rules determine the outcome ofinfluence. This problem is NP-hard and it has generated a significant amount ofrecent research on finding efficient heuristics. We focus on a {\it BalanceIndex} algorithm that relies on three parameters to tune its performance to thegiven network structure. We propose using a supervised machine-learningapproach for such tuning. We select the most influential graph features for theparameter tuning. Then, using random-walk-based graph-sampling, we create smallsnapshots from the given synthetic and large-scale real-world networks. Usingexhaustive search, we find for these snapshots the high accuracy values of BIparameters to use as a ground truth. Then, we train our machine-learning modelon the snapshots and apply this model to the real-word network to find the bestBI parameters. We apply these parameters to the sampled real-world network tomeasure the quality of the sets of initiators found this way. We use variousreal-world networks to validate our approach against other heuristic.

Learning Parameters for Balanced Index Influence Maximization