  Dynamic systems characterized by diversified evolutions are not only more
flexible, but also more resilient to attacks, failures and changing conditions.
This article addresses the quantification of the diversity of non-linear
transient dynamics obtained in undirected and unweighted complex networks as a
consequence of self-avoiding random walks. The diversity of walks starting at a
specific node $i$ is quantified in terms of a signature composed by the
entropies of the node visit probabilities along each of the initial steps. Six
theoretical models of complex networks are considered: Erd\H{o}s-R\'enyi,
Barab\'asi-Albert, Watts-Strogatz, a geographical model, as well as two
recently introduced knitted networks formed by paths. The random walk diversity
is explored at the level of network categories and of individual nodes. Because
the diversity at successive steps of the walks tends to be correlated,
principal component analysis is systematically applied in order to identify the
more relevant linear combinations of the diversity entropies and to obtain
optimal dimensionality reduction. Several interesting results are reported,
including the facts that the transient diversity tends to increase with the
average degree for all considered network models and that the Watts and
Strogatz and geographical models tend to yield diversity entropies which
increase more gradually with the number of steps, contrasting sharply with the
steep increases verified for the other four considered models. The principal
linear combination of the diversities identified by the principal component
analysis method is shown to allow an interesting characterization of individual
nodes as well as partitioning of networks into subgraphs of similar diversity.
