Link prediction accuracy on real-world networks under non-uniform missing-edge patterns

Xie He, Amir Ghasemian, Eun Lee, Alice C Schwarze, Aaron Clauset and Peter J Mucha

PLOS ONE, 2024, vol. 19, issue 7, 1-17

Abstract: 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.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0306883

DOI: 10.1371/journal.pone.0306883

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