  To a considerable extent, the continuing importance and popularity of complex
networks as models of real-world structures has been motivated by scale free
degree distributions as well as the respectively implied hubs. Being related to
sequential connections of edges in networks, paths represent another important,
dual pattern of connectivity (or motif) in complex networks (e.g., paths are
related to important concepts such as betweeness centrality). The present work
proposes a new supercategory of complex networks which are organized and/or
constructed in terms of paths. Two specific network classes are proposed and
characterized: (i) PA networks, obtained by star-path transforming
Barabasi-Albert networks; and (ii) PN networks, built by performing progressive
paths involving all nodes without repetition. Such new networks are important
not only from their potential to provide theoretical insights, but also as
putative models of real-world structures. The connectivity structure of these
two models is investigated comparatively to four traditional complex networks
models (Erdos-Renyi, Barabasi-Albert, Watts-Strogatz and a geographical model).
A series of interesting results are described, including the corroboration of
the distinct nature of the two proposed models and the importance of
considering a comprehensive set of measurements and multivariated statistical
methods for the characterization of complex networks.
