Degree-preserving randomization
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.
[3][4] There are several cases in which published research have explicitly employed degree preserving randomization in order to analyze network properties.
Dekker[5] used rewiring in order to more accurately model observed social networks by adding a secondary variable,
nodes in their simulations - Liu et al. have also used degree preserving randomization models in subsequent work exploring network controllability.
[7] Additionally, some work has been done in investigating how Degree Preserving Randomization may be used in addressing considerations of anonymity in networked data research, which has been shown to be a cause for concern in Social Network Analysis, as in the case of a study by Lewis et al.[8][9] Ultimately the work conducted by Ying and Wu, starting from a foundation of Degree Preserving Randomization, and then forwarding several modifications, has showed moderate advances in protecting anonymity without compromising the integrity of the underlying utility of the observed network.
Importantly, Degree Preserving Randomization provides a simple algorithmic design for those familiar with programming to apply a model to an available observed network.
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rule, this network would require around 67,861 individual edge rewires to construct a likely sufficiently random degree-preserved graph.
If we construct many random, degree preserving graphs from the real graph, we can then create a probability space for characteristics, such as reciprocity and average path length, and assess the degree to which the network could have expressed these characteristics at random.
As both reciprocity and average path length in this graph are normally distributed, and as the standard deviation for both reciprocity and average path length are far too narrow to include the observed case, we can reasonably posit that this network is expressing characteristics that are non-random (and thus open for further theory and modeling).
