Code evolution for link prediction in complex networks

2026-06-18

AI Summary

This study addresses the limitations of traditional handcrafted approaches to link prediction in complex networks, which often suffer from suboptimal performance and poor generalization. To overcome these challenges, the authors propose a novel code evolution framework that integrates large language models with a genetic algorithm to automatically search for and optimize the program structure of link prediction algorithms. The method innovatively explores adaptive combinations of node and link features. Extensive experiments on 580 real-world networks demonstrate that the proposed approach achieves an average AUC of 0.915, substantially outperforming existing hand-designed methods (AUC = 0.783). Furthermore, it exhibits high computational efficiency and scales effectively to networks with millions of links.

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.

Problem

• link prediction
• complex networks
• code evolution
• algorithmic innovation
• scientific discovery

Innovation

• code evolution
• link prediction
• genetic algorithms
• large language models
• automated algorithm design