  With great theoretical and practical significance, locating influential nodes
of complex networks is a promising issues. In this paper, we propose a
dynamics-sensitive (DS) centrality that integrates topological features and
dynamical properties. The DS centrality can be directly applied in locating
influential spreaders. According to the empirical results on four real networks
for both susceptible-infected-recovered (SIR) and susceptible-infected (SI)
spreading models, the DS centrality is much more accurate than degree,
$k$-shell index and eigenvector centrality.
