  Fuelled by a desire for greater connectivity, networked systems now pervade
our society at an unprecedented level that will affect it in ways we do not yet
understand. In contrast, nature has already developed efficient networks that
can instigate rapid response and consensus, when key elements are stimulated.
We present a technique for identifying these key elements by investigating the
relationships between a system's most dominant eigenvectors. This approach
reveals the most effective vertices for leading a network to rapid consensus
when stimulated, as well as the communities that form under their dynamical
influence. In applying this technique, the effectiveness of starling flocks was
found to be due, in part, to the low outdegree of every bird, where increasing
the number of outgoing connections can produce a less responsive flock. A
larger outdegree also affects the location of the birds with the most
influence, where these influentially connected birds become more centrally
located and in a poorer position to observe a predator and, hence, instigate an
evasion manoeuvre. Finally, the technique was found to be effective in large
voxel-wise brain connectomes where subjects can be identified from their
influential communities.
