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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.15140 |
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| _version_ | 1866909591992795136 |
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| author | He, Xie Ghasemian, Amir Lee, Eun Schwarze, Alice Clauset, Aaron Mucha, Peter J. |
| author_facet | He, Xie Ghasemian, Amir Lee, Eun Schwarze, Alice Clauset, Aaron Mucha, Peter J. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15140 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Link Prediction Accuracy on Real-World Networks Under Non-Uniform Missing Edge Patterns He, Xie Ghasemian, Amir Lee, Eun Schwarze, Alice Clauset, Aaron Mucha, Peter J. Dynamical Systems Social and Information Networks 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. |
| title | Link Prediction Accuracy on Real-World Networks Under Non-Uniform Missing Edge Patterns |
| topic | Dynamical Systems Social and Information Networks |
| url | https://arxiv.org/abs/2401.15140 |