PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918362107346944 |
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| author | Yu, Qi Zeng, Zhichen Yan, Yuchen Liu, Zhining Jing, Baoyu Qiu, Ruizhong Azad, Ariful Tong, Hanghang |
| author_facet | Yu, Qi Zeng, Zhichen Yan, Yuchen Liu, Zhining Jing, Baoyu Qiu, Ruizhong Azad, Ariful Tong, Hanghang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21366 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment Yu, Qi Zeng, Zhichen Yan, Yuchen Liu, Zhining Jing, Baoyu Qiu, Ruizhong Azad, Ariful Tong, Hanghang Machine Learning 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. |
| title | PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.21366 |