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Complex network topology might get pretty complicated challenging manynetwork analysis objectives, such as community detection for example. Thishowever makes common emergent network phenomena such as scale-free topology orsmall-world property even more intriguing. In the present proof-of-conceptpaper we propose a simple model of network representation inspired by a signaltransmission physical analogy, which is apparently capable of reproducing bothof the above phenomena. The model appears to be general enough to representand/or approximate arbitrary complex networks. We propose an approachconstructing such a representation by projecting each node into amulti-dimensional space of signal spectrum vectors, where network topology isinduced by their overlaps. As one of the implications this enables reducingcommunity detection in complex networks to a straightforward clustering overthe projection space, for which multiple efficient approaches are available. Webelieve such a network representation could turn out to be a useful tool formultiple network analysis objectives.

Complex network representation through multi-dimensional node projection