Network diffusion capacity unveiled by dynamical paths
arXiv:2104.10736 · doi:10.1038/s41467-023-37323-0
Abstract
Improving the understanding of diffusive processes in networks with complex topologies is one of the main challenges of today's complexity science. Each network possesses an intrinsic diffusive potential that depends on its structural connectivity. However, the diffusion of a process depends not only on this topological potential but also on the dynamical process itself. Quantifying this potential will allow the design of more efficient systems in which it is necessary either to weaken or to enhance diffusion. Here we introduce a measure, the {\em diffusion capacity}, that quantifies, through the concept of dynamical paths, the potential of an element of the system, and also, of the system itself, to propagate information. Among other examples, we study a heat diffusion model and SIR model to demonstrate the value of the proposed measure. We found, in the last case, that diffusion capacity can be used as a predictor of the evolution of the spreading process. In general, we show that the diffusion capacity provides an efficient tool to evaluate the performance of systems, and also, to identify and quantify structural modifications that could improve diffusion mechanisms.
References in corpus (33)
- Epidemic spreading in scale-free networks
- The spread of low-credibility content by social bots
- Diffusion dynamics on multiplex networks
- Influence of fake news in Twitter during the 2016 US presidential election
- The physics of spreading processes in multilayer networks
- Network Synchronization, Diffusion, and the Paradox of Heterogeneity
- Exact analytical solutions of the Susceptible-Infected-Recovered (SIR) epidemic model and of the SIR model with equal death and birth rates
- Measuring and modelling correlations in multiplex networks
- Network Geometry
- Entropy Rate of Diffusion Processes on Complex Networks
- Topology-driven instabilities: the theory of pattern formation on directed networks
- Network synchronization landscape reveals compensatory structures, quantization, and the positive effect of negative interactions
- Synchronization in networks with multiple interaction layers
- Pattern formation in multiplex networks
- Turing patterns in multiplex networks
- Effects of social distancing and isolation on epidemic spreading: a dynamical density functional theory model
- Control of Multilayer Networks
- Topology comparison of Twitter diffusion networks effectively reveals misleading information
- Laplacian Renormalization Group for heterogeneous networks
- Diffusion geometry unravels the emergence of functional clusters in collective phenomena
- Temporal profiles of avalanches on networks
- Low-dimensional behavior of Kuramoto model with inertia in complex networks
- Impact of the distribution of recovery rates on disease spreading in complex networks
- Diffusive behavior of multiplex networks
- Assessing diversity in multiplex networks
- Six Susceptible-Infected-Susceptible Models on Scale-free Networks
- Quantifying efficient information exchange in real network flows
- Functional Control of Oscillator Networks
- Diffusion geometry of multiplex and interdependent systems
- Network diffusion capacity unveiled by dynamical paths
- Self-organized explosive synchronization in complex networks: Emergence of synchronization bombs
- Synchronization of Coupled Kuramoto Oscillators under Resource Constraints
- Multilayer modeling of adoption dynamics in energy demand management