Effective Large-Scale Online Influence Maximization

Paul Lagree, Olivier Cappe, Bogdan Cautis, Silviu Maniu
2017 2017 IEEE International Conference on Data Mining (ICDM)  
In this paper, we study a highly generic version of influence maximization (IM), one of optimizing influence campaigns by sequentially selecting "spread seeds" from a set of candidates, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain "persistently" active throughout and thus do not yield further rewards. We call this problem online influence maximization with persistence. We introduce an estimator on the candidates'
more » ... mass -the expected number of nodes that can still be reached from a given seed candidate -and justify its strength to rapidly estimate the desired value. We then describe a novel algorithm, GT-UCB, relying on upper confidence bounds on the missing mass. We show that our approach leads to high-quality spreads on classic IM datasets, even though it makes almost no assumptions on the diffusion medium. Importantly, it is orders of magnitude faster than state-of-the-art IM methods.
doi:10.1109/icdm.2017.118 dblp:conf/icdm/LagreeCCM17 fatcat:kn2raojfzzd6hng6mltvvkhbxu