A network model with structured nodes

We present a network model in which words over a specific alphabet, called structures, are associated to each node and undirected edges are added depending on some distance measure between different structures. It is shown that this model can generate, without the use of preferential attachment or any other heuristic, networks with topological features similar to biological networks: power law degree distribution, low average path length, clustering coefficient independent from the network size, etc. Two biological networks: MRSA gene network and E. Coli proteinprotein interaction network, are replicated using this model.