  Complex network topology might get pretty complicated challenging many
network analysis objectives, such as community detection for example. This
however makes common emergent network phenomena such as scale-free topology or
small-world property even more intriguing. In the present proof-of-concept
paper we propose a simple model of network representation inspired by a signal
transmission physical analogy, which is apparently capable of reproducing both
of the above phenomena. The model appears to be general enough to represent
and/or approximate arbitrary complex networks. We propose an approach
constructing such a representation by projecting each node into a
multi-dimensional space of signal spectrum vectors, where network topology is
induced by their overlaps. As one of the implications this enables reducing
community detection in complex networks to a straightforward clustering over
the projection space, for which multiple efficient approaches are available. We
believe such a network representation could turn out to be a useful tool for
multiple network analysis objectives.
