  We study the dynamics of randomly connected networks composed of binary
Boolean elements and those composed of binary majority vote elements. We
elucidate their differences in both sparsely and densely connected cases. The
quickness of large network dynamics is usually quantified by the length of
transient paths, an analytically intractable measure. For discrete-time
dynamics of networks of binary elements, we address this dilemma with an
alternative unified framework by using a concept termed state concentration,
defined as the exponent of the average number of t-step ancestors in state
transition graphs. The state transition graph is defined by nodes corresponding
to network states and directed links corresponding to transitions. Using this
exponent, we interrogate the dynamics of random Boolean and majority vote
networks. We find that extremely sparse Boolean networks and majority vote
networks with arbitrary density achieve quickness, owing in part to long-tailed
in-degree distributions. As a corollary, only relatively dense majority vote
networks can achieve both quickness and robustness.
