It serves as a validation for pore-network models and the methods used to generate networks.
Broadly speaking, there are two approaches to generate networks from data.
The binary heap consistently took significantly less time than the treap-based methods to generate networks of several different sizes.
Preferential attachment models generate networks by sequentially introducing nodes that prefer to attach to nodes with high degree.
Actor selections, over time, generate networks that partition group members into stable, balanced subsets at equilibrium or near equilibrium.
In the first step, we generate networks in a random and unbiased way using atomistic Monte Carlo (MC) simulations in the (N,P,T) isothermal-isobaric statistical ensemble [9, 10].
The sampling method 1 generates networks that are more similar than the data, and the other two methods generate networks that are less similar (with the exception of the LH10 case, and the method 2 in the InVS13 case).
These simulations generate networks that show (i) a small diameter of at most 4, (i i) a high clustering coefficient (with respect to edge density), and (i i i) a power-law degree distribution.
Networks exhibit small world properties when "global" connectivity patterns generate networks wherein all actors connect to each other through relatively short chains typically, six or fewer segments connect all actors in the network (e.g., Granovetter 2003; Watts and Strogatz 1998).
We change the value of d0 (and typically keep P0=0.9) to generate networks with different number of edges and different maximum hop distances, as described later in this section.
The proposed mechanism is demonstrated to be able to generate networks with features of power-law, small-world, and modularity, which represents more realistic properties of actual software networks.
