Taking into account the classic emblem “nothing in biology makes sense except in the light of evolution” \cite{Dobzhansky_1973}, we should expect most systems -and therefore their mechanisms- would present highly conserved aspects across different levels, scales, species, and types of measurements. Hence, in the light of evolution, most systems could be thought of as the result
- and ontogenic processes, time-and-space-locked to the physical world's regularities \cite{Parada_2018}. Many real-world complex networks indeed present topological and geometrical properties, such as short path length, high clustering, high-degree nodes, modularity, among others \cite{Bullmore_2009}. An evolutionary perspective would suggest that such pervasive properties provide functional advantages such as effective information transfer, robustness, and metabolical economy. Experimental evidence suggests this is actually the case, for example, \citet{Buzs_ki_2013} found that despite relevant brain size differences across mammalian species, the hierarchical organization of functional brain dynamics is surprisingly well preserved. In other words,
regardless
brain size, neural interactions unfold over similar time-scales within and across brain networks. Hence, a reasonable hypothesis would predict that such properties would also be found in graphs representing mechanisms.