title: Hodge Decomposition of Information Flow on Small-World Networks.
abstract: We investigate the influence of the small-world topology on the composition of information flow on networks. By appealing to the combinatorial Hodge theory, we decompose information flow generated by random threshold networks on the Watts-Strogatz model into three components: gradient, harmonic and curl flows. The harmonic and curl flows represent globally circular and locally circular components, respectively. The Watts-Strogatz model bridges the two extreme network topologies, a lattice network and a random network, by a single parameter that is the probability of random rewiring. The small-world topology is realized within a certain range between them. By numerical simulation we found that as networks become more random the ratio of harmonic flow to the total magnitude of information flow increases whereas the ratio of curl flow decreases. Furthermore, both quantities are significantly enhanced from the level when only network structure is considered for the network close to a random network and a lattice network, respectively. Finally, the sum of these two ratios takes its maximum value within the small-world region. These findings suggest that the dynamical information counterpart of global integration and that of local segregation are the harmonic flow and the curl flow, respectively, and that a part of the small-world region is dominated by internal circulation of information flow.
authors.affiliation: Department of Planetology, Graduate School of Science, Kobe University Kobe, Japan.
authors.forename: Taichi
authors.initials: T
authors.lastname: Haruna
authors.affiliation: Department of Planetology, Graduate School of Science, Kobe University Kobe, Japan.
authors.forename: Yuuya
authors.initials: Y
authors.lastname: Fujiki
mesh_terms: D000818:Animals; D006801:Humans; D008962:Models, Theoretical; D009415:Nerve Net; D016571:Neural Networks, Computer
keywords: Hodge decomposition; functional brain networks; random threshold network; small-world network; transfer entropy