Abstract
Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Long overlooked in this search has been the role of the innate: networks that connect based on identity-dependent compatibility rules. Inspired by the genetic principles that guide brain connectivity, we derive a network encoding process that can utilize wiring rules to reproducibly generate specific topologies. To illustrate the representational power of this approach, we propose stochastic and deterministic processes for generating a wide range of network topologies. Specifically, we detail network heuristics that generate structured graphs, such as feed-forward and hierarchical networks. In addition, we characterize a Random Genetic (RG) family of networks, which, like Erdős–Rényi graphs, display critical phase transitions, however their modular underpinnings lead to markedly different behaviors under targeted attacks. The proposed framework provides a relevant null-model for social and biological systems, where diverse metrics of identity underpin a node’s preferred connectivity.
Highlights
Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies
Drawing inspiration from the physical laws apparent in brain connectivity, we propose using genetic encoding as a general way to construct a network, thereby defining a generative model that results in topologies built out of a set of modular components
To illustrate the genetic encoding of a network, we show how wiring rules can provide the basis for two well-known and highly dissimilar architectures: a scale-free and a feed-forward network
Summary
Our understanding of real-world connected systems has benefited from studying their evolution, from random wirings and rewirings to growth-dependent topologies. Generative models in network science are often designed to recreate unique topologies observed in nature[1,2,3,4] Such approaches range from the random edge insertion of the Erdős–Rényi model[5] to the preferential attachment model[6], which aims to capture the growth of certain real-world networks. A direct mapping can be found between a neuron’s (node’s) identity and its c onnectivity[10] This can be seen as a specific network encoding method: rather than producing a family of networks, a known set of genetic interactions reproducibly generates a single graph. Our work elucidates how genetic encoding can balance the generation of structured graphs with the exploration of new topologies through stochastic processes
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
