用代码演化生成的链接预测算法优于人工设计，且更高效。
Code evolution for link prediction in complex networks
- 通过遗传算法自动演化出新型链接预测算法。
- 在580个网络上平均AUC达0.915，高于人工方法的0.783。
- 适合需要高效、自动化算法创新的研究者使用。
链接预测在复杂网络中广泛存在，已有多种人工设计的方法。本文利用这一丰富的程序空间，探索自动化代码演化系统在生成机器设计的链接预测方法上的表现与行为。尽管训练数据有限，通过代码演化生成的算法在580个网络上平均AUC达到0.915，显著优于人工方法的0.783，并展现出更好的计算效率，可应用于包含数百万边的网络。所发现的方法虽沿用人类常用策略，但在节点特征与边特征的选择和组合上具有关键创新。这展示了现代大语言模型与遗传算法在算法创新与科学发现中的潜力。
原文摘要 · Abstract (English)
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.
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