  We introduce cluster dynamical models of conflicts in which only the largest
cluster can be involved in an action. This mimics the situations in which an
attack is planned by a central body, and the largest attack force is used. We
study the model in its annealed random graph version, on a fixed network, and
on a network evolving through the actions. The sizes of actions are distributed
with a power-law tail, however, the exponent is non-universal and depends on
the frequency of actions and sparseness of the available connections between
units. Allowing the network reconstruction over time in a self-organized
manner, e.g., by adding the links based on previous liaisons between units, we
find that the power-law exponent depends on the evolution time of the network.
Its lower limit is given by the universal value 5/2, derived analytically for
the case of random fragmentation processes. In the temporal patterns behind the
size of actions we find long-range correlations in the time series of number of
clusters and non-trivial distribution of time that a unit waits between two
actions. In the case of an evolving network the distribution develops a
power-law tail, indicating that through the repeated actions, the system
develops internal structure which is not just more effective in terms of the
size of events, but also has a full hierarchy of units.
