Code evolution for link prediction in complex networks
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Computer Science > Social and Information Networks
Title:Code evolution for link prediction in complex networks
Abstract: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 methods, but contain key innovations in the selection and combination of node- and link-features. This illustrates the role modern large language models and genetic algorithms can play in algorithmic innovation and scientific discovery more generally.
| Subjects: | Social and Information Networks (cs.SI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.26132 [cs.SI] |
| (or arXiv:2606.26132v1 [cs.SI] for this version) | |
| https://doi.org/10.48550/arXiv.2606.26132
arXiv-issued DOI via DataCite
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