Network alignment (NA) aims to identify node correspondence across different networks and serves as a critical cornerstone behind various downstream multi-network learning tasks. Despite growing research in NA, there lacks a comprehensive library that facilitates the systematic development and benchmarking of NA methods. In this work, we introduce PLANETALIGN, a comprehensive Python library for network alignment that features a rich collection of built-in datasets, methods, and evaluation pipelines with easy-to-use APIs. Specifically, PLANETALIGN integrates 18 datasets and 14 NA methods with extensible APIs for easy use and development of NA methods. Our standardized evaluation pipeline encompasses a wide range of metrics, enabling a systematic assessment of the effectiveness, scalability, and robustness of NA methods. Through extensive comparative studies, we reveal practical insights into the strengths and limitations of existing NA methods. We hope that PLANETALIGN can foster a deeper understanding of the NA problem and facilitate the development and benchmarking of more effective, scalable, and robust methods in the future. The source code of PLANETALIGN is available at https://github.com/yq-leo/PlanetAlign.

翻译：网络对齐（NA）旨在识别不同网络间的节点对应关系，是支撑各类下游多网络学习任务的关键基石。尽管网络对齐研究日益增长，但目前仍缺乏一个能够系统化促进网络对齐方法开发与基准测试的综合性工具库。本工作中，我们推出了PLANETALIGN——一个功能全面的网络对齐Python库，其内置了丰富的数据集、方法及评估流程，并提供易于使用的应用程序接口。具体而言，PLANETALIGN集成了18个数据集与14种网络对齐方法，并通过可扩展的应用程序接口支持网络对齐方法的便捷使用与开发。我们标准化的评估流程涵盖广泛的评价指标，能够系统性地评估网络对齐方法的有效性、可扩展性与鲁棒性。通过大量对比研究，我们揭示了现有网络对齐方法优势与局限性的实践洞见。我们希望PLANETALIGN能够促进对网络对齐问题的深入理解，并推动未来开发与评估更高效、可扩展且鲁棒的网络对齐方法。PLANETALIGN的源代码发布于 https://github.com/yq-leo/PlanetAlign。