  We study corruption as a generalized epidemic process on the graph of social
relationships. The main difference to classical epidemic processes is the
strong nonlinear dependence of the transmission probability on the local
density of corruption and the mean field influence of the overall corruption in
the society. Network clustering and the degree-degree correlation play an
essential role in corruption dynamics. We discuss phase transitions, the
influence of the graph structure and the implications for epidemic control.
Structural and dynamical arguments are given why strongly hierarchically
organized societies like systems with dictatorial tendency are more vulnerable
to corruption than democracies. A similar type of modelling can be applied to
other social contagion spreading processes like opinion formation, doping
usage, social disorders or innovation dynamics.
