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In order to conduct analyses of networked systems where connections betweenindividuals take on a range of values - counts, continuous strengths or ordinalrankings - a common technique is to dichotomize the data according to theirpositions with respect to a threshold value. However, there are two issues toconsider: how the results of the analysis depend on the choice of threshold,and what role the presence of noise has on a system with respect to a fixedthreshold value. We show that while there are principled criteria of keepinginformation from the valued graph in the dichotomized version, they producesuch a wide range of binary graphs that only a fraction of the relevantinformation will be kept. Additionally, while dichotomization of predictors inlinear models has a known asymptotic efficiency loss, the same process appliedto network edges in a time series model will lead to an efficiency loss thatgrows larger as the network increases in size.

Valued Ties Tell Fewer Lies: Why Not To Dichotomize Network Edges With Thresholds