  Discovery processes have been an important topic in the network science
field. The exploration of nodes can be understood as the knowledge acquisition
process taking place in the network, where nodes represent concepts and edges
are the semantical relationships between concepts. While some studies have
analyzed the performance of the knowledge acquisition process in particular
network topologies, here we performed a systematic performance analysis in
well-known dynamics and topologies. Several interesting results have been
found. Overall, all learning curves displayed the same learning shape, with
different speed rates. We also found ambiguities in the feature space
describing the learning curves, meaning that the same knowledge acquisition
curve can be generated in different combinations of network topology and
dynamics. A surprising example of such patterns are the learning curves
obtained from random and Waxman networks: despite the very distinct
characteristics in terms of global structure, several curves from different
models turned out to be similar. All in all, our results suggest that different
learning strategies can lead to the same learning performance. From the network
reconstruction point of view, however, this means that learning curves of
observed sequences should be combined with other sequence features if one aims
at inferring network topology from observed sequences.
