Abstract

In this article the different features exhibited by four types of natural and artificial networks are reviewed, after a brief account of the basic quantitative characterizations that allow to measure network complexity. Some key questions that will be explored are: 1. What mechanisms have originated observed topological regularities in complex networks? 2. To what extent does optimization shape network topology ? 3. What is the origin of homeostasis in complex networks? 4. Is homeostasis a driving force or a side effect in network topology? 5. Is tinkering an inevitable component of network evolution ? 6. Are engineered systems free of tinkering? Comparison between the mechanisms that drive the building process of different graphs reveals that optimization might be a driving force, canalized in biological systems by both tinkering and the presence of conflicting constraints common to any hard multidimensional optimization process. Conversely, the presence of global features in technology graphs that closely resemble those observed in biological webs indicates that, in spite of the engineered design that should lead to hierarchical structures (such as the one shown in Figure 1) the tinkerer seems to be at work. 2. MEASURING NETWORK COMPLEXITY Since we are interested in comparing the global features of both biological and artificial (engineered) networks, we need to consider a number of quantitative measures in order to characterize them properly. In order to do so, the network structure is represented by a graph #, as before. Some of these measures (minimal distance, clustering coefficient) are usually applied to topological (i.e., static) descriptors of the graph structure, but others (entropy, redundancy, degeneracy) also apply to states that average dynamic variables

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