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

On the Diversity of Non-Linear Transient Dynamics in Several Types of Complex Networks