  We investigate the problem of locating the source of a self-interacting
signal spreading in a complex networks. We use a well-known rumour model as an
example of the process with self-interaction. According to this model based on
the SIR epidemic dynamics, the infected nodes may interact and discourage each
other from gossiping with probability $\alpha$. We compare three algorithms of
source localization: Limited Pinto-Thiran-Vettarli (LPTV), Gradient Maximum
Likelihood (GMLA) and one based on Pearson correlation between time and
distance. The results of numerical simulations show that additional
interactions between infected nodes decrease the quality of LPTV and Pearson.
GMLA is the most resistant to harmful effects of the self-interactions, which
is especially visible for medium and high level of stochasticity of the
process, when spreading rate is below 0.5. The reason for this may be the fact
that GMLA uses only the nearest observers, which are much less likely affected
by the interactions between infected nodes, because these contacts become
important as the epidemics develops and the number of infected agents
increases.
