  Abrupt, system-wide transitions can be endogenously generated by seemingly
stable networks of interacting dynamical units, such as mode switching in
neuronal networks or public opinion changes in social systems. However, it
remains poorly understood how such `noise-induced transitions' emerge from the
interplay of network structure and dynamics on the network. Here, we report on
two key roles that nodes can play in the progression towards noise-induced
tipping points. The models used are dynamical networks where the nodes are
governed by the Boltzmann-Gibbs distribution, but the concept is easily
generalized. First, so-called `initiator nodes' absorb and then transmit
short-lived fluctuations to neighboring nodes, making them temporarily more
dynamic. These neighbor nodes can then in turn transmit fluctuations to their
neighbors, and so on, leading to a domino-effect where the more stable a node
is (i.e., high average free energy barrier), the more neighbors are needed that
have become temporarily dynamic. Interestingly, towards the tipping point we
identify so-called `stabilizer nodes' whose state information becomes part of
the long-term memory of the system, after which the domino-effect is reversed
and settles the node in their new stable attractor. We validate these roles by
targeted interventions that make tipping points more (or less) likely to begin
or lead to systemic change. This opens up possibilities for understanding and
controlling endogenously generated metastable behavior.
