A network model with structured nodes

TL;DR

This paper introduces a network model where nodes are associated with structured words, and edges are formed based on structural distance, successfully replicating biological network features without heuristics.

Contribution

The model generates biologically relevant network topologies solely based on structural node properties, without relying on preferential attachment.

Findings

Replicates biological network features such as power law degree distribution.

Produces clustering coefficients independent of network size.

Successfully models C. Elegans neural and E. Coli protein interaction networks.

Abstract

We present a network model in which words over a specific alphabet, called {\it structures}, are associated to each node and undirected edges are added depending on some distance 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, clustering coefficient independent from the network size, etc. Specific biological networks ({\it C. Elegans} neural network and {\it E. Coli} protein-protein interaction network) are replicated using this model.