  Well-established methods of locating the source of information in a complex
network are usually derived with the assumption of complete and exact knowledge
of network topology. We study the performance of three such algorithms (LPTVA,
GMLA and Pearson correlation algorithm) in scenarios that do not fulfill this
assumption by modifying the network prior to localization. This is done by
adding superfluous new links, hiding existing ones, or reattaching links in
accordance with the network's structural Hamiltonian. We find that GMLA is
highly resilient to the addition of superfluous edges, as its precision falls
by more than statistical uncertainty only when the number of links is
approximately doubled. On the other hand, if the edge set is underestimated or
reattachment has taken place, the performance of GMLA drops significantly. In
such a scenario the Pearson algorithm is preferable, retaining most of its
performance when other simulation parameters favor localization (high density
of observers, highly deterministic propagation). It is also generally more
accurate than LPTVA, as well as orders of magnitude faster. The aforementioned
differences between localization algorithms can be intuitively explained,
although a need for further theoretical research is noted.
