SyntheticNoNs.jl documentation

NoNLevel{GT <: AbstractGraph{T}}(upper::Tuple{GT, T}, self::GT, lower::Vector{GT})

Create a level of a NoN. self is the network at this level. upper is a tuple of the network at the higher level and the node from that level whose network is self. lower is a vector of networks corresponding to each node in self. Due to memory, it may be wiser to store a NoN as names of networks.

barabasi_albert_nm(n::Integer, m::Integer; tries=3, seed=-1, keyargs...)

Can also call this using gen_sf with the same arguments. Generate a Barabási–Albert random graph with n nodes and as close to m edges as possible (note that due to how such graphs are constructed, certain target densities are difficult to achieve). Throws an error when it is not mathematically possible.

Optional arguments

• tries=3: number of regenerations to try if the graph is not connected. Returns if the graph is connected or if all tries are used.
• keyargs...: see LightGraphs' barabasi_albert

Implementation notes

m = nk - k^2, where k is the parameter from LightGraphs' barabasi_albert method. Thus, we use Polynomials.jl to solve for k. However, since k must be an integer to be inputted into barabasi_albert, rounding is needed and thus achieving an exact match with m is not always possible.

euclidean_graph_nm()

Can also call this using gen_geo with the same arguments. Generate a geometric random graph with n nodes and as close to m edges as possible. Throws an error when it is not mathematically possible.

Optional arguments

Implementation notes

We generated a bunch of euclidean_graphs and then reverse engineered the number of nodes and cutoff needed to reach a target number of edges. Deriving the theoretical expected value given the number of nodes and cutoff would be more optimal.