Spatially embedded growing small-world networks.
Networks in nature are often formed within a spatial domain in a dynamical manner, gaining links and nodes as they develop over time. Motivated by the growth and development of neuronal networks, we propose a class of spatially-based growing network models and investigate the resulting statistical network properties as a function of the dimension and topology of the space in which the networks are embedded. In particular, we consider two models in which nodes are placed one by one in random locations in space, with each such placement followed by configuration relaxation toward uniform node density, and connection of the new node with spatially nearby nodes. We find that such growth processes naturally result in networks with small-world features, including a short characteristic path length and nonzero clustering. We find no qualitative differences in these properties for two different topologies, and we suggest that results for these properties may not depend strongly on the topology of the embedding space. The results do depend strongly on dimension, and higher-dimensional spaces result in shorter path lengths but less clustering.
Institute for Research in Electronics and Applied Physics University of Maryland, College Park, Maryland 20742, USA.
Ari
A
Zitin
Institute for Research in Electronics and Applied Physics University of Maryland, College Park, Maryland 20742, USA.
Alexander
A
Gorowara
Institute for Research in Electronics and Applied Physics University of Maryland, College Park, Maryland 20742, USA.
Shane
S
Squires
Heron Systems Inc, 2121 Eisenhower Ave, Suite 401 Alexandria VA 22307.
Mark
M
Herrera
Institute for Research in Electronics and Applied Physics University of Maryland, College Park, Maryland 20742, USA.
Thomas M
TM
Antonsen
Institute for Research in Electronics and Applied Physics University of Maryland, College Park, Maryland 20742, USA.
Michelle
M
Girvan
Institute for Research in Electronics and Applied Physics University of Maryland, College Park, Maryland 20742, USA.
Edward
E
Ott
D000465:Algorithms; D008962:Models, Theoretical