TY - JOUR

T1 - Random rectangular graphs

AU - Estrada, Ernesto

AU - Sheerin, Matthew

PY - 2015/4/21

Y1 - 2015/4/21

N2 - A generalization of the random geometric graph (RGG) model is proposed by considering a set of points uniformly and independently distributed on a rectangle of unit area instead of on a unit square [0, 1]2 . The topological properties of the random rectangular graphs (RRGs) generated by this model are then studied as a function of the rectangle sides lengths a and b = 1/a, and the radius r used to connect the nodes. When a = 1 we recover the RGG, and when a → ∞ the very elongated rectangle generated resembles a one-dimensional RGG. We obtain here analytical expressions for the average degree, degree distribution, connectivity, average path length and clustering coefficient for RRG. These results provide evidence that show that most of these properties depend on the connection radius and the side length of the rectangle, usually in a monotonic way. The clustering coefficient, however, increases when the square is transformed into a slightly elongated rectangle, and after this maximum it decays with the increase of the elongation of the rectangle. We support all our findings by computational simulations that show the goodness of the theoretical models proposed for RRGs.

AB - A generalization of the random geometric graph (RGG) model is proposed by considering a set of points uniformly and independently distributed on a rectangle of unit area instead of on a unit square [0, 1]2 . The topological properties of the random rectangular graphs (RRGs) generated by this model are then studied as a function of the rectangle sides lengths a and b = 1/a, and the radius r used to connect the nodes. When a = 1 we recover the RGG, and when a → ∞ the very elongated rectangle generated resembles a one-dimensional RGG. We obtain here analytical expressions for the average degree, degree distribution, connectivity, average path length and clustering coefficient for RRG. These results provide evidence that show that most of these properties depend on the connection radius and the side length of the rectangle, usually in a monotonic way. The clustering coefficient, however, increases when the square is transformed into a slightly elongated rectangle, and after this maximum it decays with the increase of the elongation of the rectangle. We support all our findings by computational simulations that show the goodness of the theoretical models proposed for RRGs.

KW - random geometric graph

KW - rectangle length

KW - clustering coefficient

KW - path length

U2 - 10.1103/PhysRevE.91.042805

DO - 10.1103/PhysRevE.91.042805

M3 - Article

VL - 91

JO - Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics

JF - Physical Review E: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics

SN - 1539-3755

IS - 4

M1 - 042805

ER -