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A major challenge in network science is to determine parameters governingcomplex network dynamics from experimental observations and theoretical models.In complex chemical reaction networks, for example, such as those describingprocesses in internal combustion engines and power generators, rate constantestimates vary significantly across studies despite substantial experimentalefforts. Here, we examine the possibility that variability in measuredconstants can be largely attributed to the impact of missing networkinformation on parameter estimation. Through the numerical simulation ofmeasurements in incomplete chemical reaction networks, we show thatunaccountability of network links presumed unimportant (with local sensitivityamounting to less than two percent of that of a measured link) can createapparent rate constant variations as large as one order of magnitude even if noexperimental errors are present in the data. Furthermore, the correlationcoefficient between the logarithmic deviation of the rate constant estimate andthe cumulative relative sensitivity of the neglected reactions was less than$0.5$ in all cases. Thus, for dynamical processes on complex networks,iteratively expanding a model by determining new parameters from data collectedunder specific conditions is unlikely to produce reliable results.

Missing links as a source of seemingly variable constants in complex reaction networks