A spectrum of routing strategies for brain networks.

ABSTRACT: Communication of signals among nodes in a complex network poses fundamental problems of efficiency and cost. Routing of messages along shortest paths requires global information about the topology, while spreading by diffusion, which operates according to local topological features, is informationally "cheap" but inefficient. We introduce a stochastic model for network communication that combines local and global information about the network topology to generate biased random walks on the network. The model generates a continuous spectrum of dynamics that converge onto shortest-path and random-walk (diffusion) communication processes at the limiting extremes. We implement the model on two cohorts of human connectome networks and investigate the effects of varying the global information bi

SUBMITTER: Avena-Koenigsberger A 

PROVIDER: S-EPMC6426276 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature