Degree-preserving randomization: Difference between revisions
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'''Degree Preserving Randomization''' is a technique used in [[Network Science]] that aims to assess whether or not variations observed in a given graph could simply be an artifact of the graph's inherent structural properties rather than properties unique to the nodes, in an observed network. |
'''Degree Preserving Randomization''' is a technique used in [[Network Science]] that aims to assess whether or not variations observed in a given graph could simply be an artifact of the graph's inherent structural properties rather than properties unique to the nodes, in an observed network. |
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==Background== |
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Cataloged as early as 1996 <ref>{{cite journal |last1= Rao |first1= A Ramachandra |last2= Jana |first2= Rabindranath |last3= Bandyopadhyay |first3= Suraj |year= 1996 |title= A Markov chain Monte Carlo method for generating random (0, 1)-matrices with given marginals |journal=Indian Journal of Statistics Series A |url=http://sankhya.isical.ac.in/search/58a2/58a2021.pdf |accessdate=November 5, 2014}}</ref>, the most simple implementation of degree preserving randomization relies on a [[Monte Carlo]] algorithm that rearranges, or "rewires" the network at random such that, with a sufficient number of rewires, the network's degree distribution is identical to the initial degree distribution of the network, though the topological structure of the network has become completely distinct from the original network. |
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Revision as of 06:37, 6 November 2014
Degree Preserving Randomization is a technique used in Network Science that aims to assess whether or not variations observed in a given graph could simply be an artifact of the graph's inherent structural properties rather than properties unique to the nodes, in an observed network.
Background
Cataloged as early as 1996 [1], the most simple implementation of degree preserving randomization relies on a Monte Carlo algorithm that rearranges, or "rewires" the network at random such that, with a sufficient number of rewires, the network's degree distribution is identical to the initial degree distribution of the network, though the topological structure of the network has become completely distinct from the original network.
- ^ Rao, A Ramachandra; Jana, Rabindranath; Bandyopadhyay, Suraj (1996). "A Markov chain Monte Carlo method for generating random (0, 1)-matrices with given marginals" (PDF). Indian Journal of Statistics Series A. Retrieved November 5, 2014.