Code evolution for link prediction in complex networks
The problem of predicting links in complex networks appears in different disciplines and has led to a variety of ingenious human-designed methods. We use this rich program space to explore the performance and behavior of automated code-evolution systems tasked to obtain machine-designed methods for link prediction. Despite being trained on limited data, algorithms evolved through code evolution outperform human-designed methods (with an average AUC score of 0.915 vs. 0.783, computed over 580 networks) and show improved computational efficiency, allowing them to be applied to networks with millions of links. The discovered methods follow approaches that have been employed in human-designed...
| Authors | Alexey Vlaskin, Eduardo G. Altmann |
|---|---|
| Primary category | cs.SI |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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