  Many real-world systems give rise to a time series of symbols. The elements
in a sequence can be generated by agents walking over a networked space so that
whenever a node is visited the corresponding symbol is generated. In many
situations the underlying network is hidden, and one aims to recover its
original structure and/or properties. For example, when analyzing texts, the
underlying network structure generating a particular sequence of words is not
available. In this paper, we analyze whether one can recover the underlying
local properties of networks generating sequences of symbols for different
combinations of random walks and network topologies. We found that the
reconstruction performance is influenced by the bias of the agent dynamics.
When the walker is biased toward high-degree neighbors, the best performance
was obtained for most of the network models and properties. Surprisingly, this
same effect is not observed for the clustering coefficient and eccentric, even
when large sequences are considered. We also found that the true self-avoiding
displayed similar performance as the one preferring highly-connected nodes,
with the advantage of yielding competitive performance to recover the
clustering coefficient. Our results may have implications for the construction
and interpretation of networks generated from sequences.
