Physics – Condensed Matter – Disordered Systems and Neural Networks
Scientific paper
2001-12-09
Physica A, volume 301, pp 601-619 (2001)
Physics
Condensed Matter
Disordered Systems and Neural Networks
23 pages, 5 figures, 2 tables, typographical error corrected
Scientific paper
10.1016/S0378-4371(01)00430-7
The competition between local and global driving forces is significant in a wide variety of naturally occurring branched networks. We have investigated the impact of a global minimization criterion versus a local one on the structure of spanning trees. To do so, we consider two spanning tree structures - the generalized minimal spanning tree (GMST) defined by Dror et al. [1] and an analogous structure based on the invasion percolation network, which we term the generalized invasive spanning tree or GIST. In general, these two structures represent extremes of global and local optimality, respectively. Structural characteristics are compared between the GMST and GIST for a fixed lattice. In addition, we demonstrate a method for creating a series of structures which enable one to span the range between these two extremes. Two structural characterizations, the occupied edge density (i.e., the fraction of edges in the graph that are included in the tree) and the tortuosity of the arcs in the trees, are shown to correlate well with the degree to which an intermediate structure resembles the GMST or GIST. Both characterizations are straightforward to determine from an image and are potentially useful tools in the analysis of the formation of network structures.
Kansal Anuraag R.
Torquato Salvatore
No associations
LandOfFree
Globally and Locally Minimal Weight Spanning Tree Networks does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Globally and Locally Minimal Weight Spanning Tree Networks, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Globally and Locally Minimal Weight Spanning Tree Networks will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-317705