Alternative paths in a network play an important role in its functionality as they can maintain the information flow under node/link failures. In this paper we explore the navigation of a network taking into account the alternative paths and in particular how can we describe this navigation in a concise way. Our approach is to simplify the network by aggregating into groups the nodes that do not contribute to alternative paths. We refer to these groups as super-nodes, and describe the post-aggregation network with super-nodes as the skeleton network. We present a method to describe with the least amount of information the paths in the super-nodes and skeleton network. Applying our method to several real networks we observed that there is scaling behaviour between the information required to describe all the paths in a network and the minimal information to describe the paths of its skeleton. We show how from this scaling we can evaluate the information of the paths for large networks with less computational cost.
Year
2020
URL
Abstract
Summary
A method for simplifying a network by grouping nodes that do not contribute to alternative paths into “super-nodes”, producing a compact “skeleton network” that still captures how path diversity supports robustness to node and link failures. The paper shows a scaling relationship between the information needed to describe all paths in a network and the minimal information needed for its skeleton, letting large networks’ path information be evaluated at far lower computational cost.
bibtex
@article{yin2020simplification,
author = {H. Yin and Richard G. Clegg and R. J. Mondrag\'on},
title = {Simplification of networks by conserving path diversity and minimisation of the search information},
journal = {Scientific Reports},
year = {2020},
volume = {10},
number = {1},
pages = {19150},
doi = {10.1038/s41598-020-75741-y}
}
author = {H. Yin and Richard G. Clegg and R. J. Mondrag\'on},
title = {Simplification of networks by conserving path diversity and minimisation of the search information},
journal = {Scientific Reports},
year = {2020},
volume = {10},
number = {1},
pages = {19150},
doi = {10.1038/s41598-020-75741-y}
}
Venue
Scientific Reports, 10(1), article 19150