Create a Brownian proximity network. size nodes are places randomly in two
dimensions, and connected by links if their distance does not exceed radius.
The size of the playing field is chosen such that the expected number of neighbours of
each node is avg_degree.
The resulting network is temporal, i.e. evolves over time. Non-infected nodes diffuse with diffusivity \(D_0\), infected nodes with diffusivity \(D_1\). As the number of infected nodes grows, diffusivities are additionally scaled by the factor \((1 - N_{inf} / N)^{\gamma}\).
The network evolves in descreet time steps of length dt. If left unspecified, a suitable
dt is chosen automatically based on the diffusivities and contact radius.
Usage
brownian_proximity_temporalnetwork(
size,
avg_degree,
radius,
D0,
D1 = NULL,
gamma = 0,
dt = NULL
)Arguments
- size
the number of nodes in the network
- avg_degree
the average number of neighbours of each node
- radius
the contact radius, i.e. the distance below which two nodes are connected by links
- D0
the diffusivity of non-infected nodes
- D1
the diffusivity of infected nodes
- gamma
the exponenent which with diffusivities are scaled as the epidemic grows
- dt
the time step used when evolving the network