  One of the most central questions in network science is: which nodes are most
important? Often this question is answered using structural properties such as
high connectedness or centrality in the network. However, static structural
connectedness does not necessarily translate to dynamical importance. To
demonstrate this, we simulate the kinetic Ising spin model on generated
networks and one real-world weighted network. The dynamic impact of nodes is
assessed by causally intervening on node state probabilities and measuring the
effect on the systemic dynamics. The results show that structural features such
as network centrality or connectedness are actually poor predictors of the
dynamical impact of a node on the rest of the network. A solution is offered in
the form of an information theoretical measure named integrated mutual
information. The metric is able to accurately predict the dynamically most
important node ('driver' node) in networks based on observational data of
non-intervened dynamics. We conclude that the driver node(s) in networks are
not necessarily the most well-connected or central nodes. Indeed, the common
assumption of network structural features being proportional to dynamical
importance is false. Consequently, great care should be taken when deriving
dynamical importance from network data alone. These results highlight the need
for novel inference methods that take both structure and dynamics into account.
