  Influence Maximization (IM) aims at finding the most influential users in a
social network, i. e., users who maximize the spread of an opinion within a
certain propagation model. Previous work investigated the correlation between
influence spread and nodal centrality measures to bypass more expensive IM
simulations. The results were promising but incomplete, since these studies
investigated the performance (i. e., the ability to identify influential users)
of centrality measures only in restricted settings, e. g., in
undirected/unweighted networks and/or within a propagation model less common
for IM. In this paper, we first show that good results within the Susceptible-
Infected-Removed (SIR) propagation model for unweighted and undirected networks
do not necessarily transfer to directed or weighted networks under the popular
Independent Cascade (IC) propagation model. Then, we identify a set of
centrality measures with good performance for weighted and directed networks
within the IC model. Our main contribution is a new way to combine the
centrality measures in a closed formula to yield even better results.
Additionally, we also extend gravitational centrality (GC) with the proposed
combined centrality measures. Our experiments on 50 real-world data sets show
that our proposed centrality measures outperform well-known centrality measures
and the state-of-the art GC measure significantly. social networks, influence
maximization, centrality measures, IC propagation model, influential spreaders
