  Real-world network datasets are typically obtained in ways that fail to
capture all edges. The patterns of missing data are often non-uniform as they
reflect biases and other shortcomings of different data collection methods.
Nevertheless, uniform missing data is a common assumption made when no
additional information is available about the underlying missing-edge pattern,
and link prediction methods are frequently tested against uniformly missing
edges. To investigate the impact of different missing-edge patterns on link
prediction accuracy, we employ 9 link prediction algorithms from 4 different
families to analyze 20 different missing-edge patterns that we categorize into
5 groups. Our comparative simulation study, spanning 250 real-world network
datasets from 6 different domains, provides a detailed picture of the
significant variations in the performance of different link prediction
algorithms in these different settings. With this study, we aim to provide a
guide for future researchers to help them select a link prediction algorithm
that is well suited to their sampled network data, considering the data
collection process and application domain.
