  Influence maximization (IM) is the problem of finding a seed vertex set that
maximizes the expected number of vertices influenced under a given diffusion
model. Due to the NP-Hardness of finding an optimal seed set, approximation
algorithms are frequently used for IM. In this work, we describe a fast,
error-adaptive approach that leverages Count-Distinct sketches and hash-based
fused sampling. To estimate the number of influenced vertices throughout a
diffusion, we use per-vertex Flajolet-Martin sketches where each sketch
corresponds to a sampled subgraph. To efficiently simulate the diffusions, the
reach-set cardinalities of a single vertex are stored in memory in a
consecutive fashion. This allows the proposed algorithm to estimate the number
of influenced vertices in a single step for simulations at once. For a faster
IM kernel, we rebuild the sketches in parallel only after observing estimation
errors above a given threshold. Our experimental results show that the proposed
algorithm yields high-quality seed sets while being up to 119x faster than a
state-of-the-art approximation algorithm. In addition, it is up to 62x faster
than a sketch-based approach while producing seed sets with 3%-12% better
influence scores
