PLANETALIGN: A Comprehensive Python Library for Benchmarking Network Alignment

2025-05-27

AI Summary

The network alignment (NA) field lacks a unified, scalable, open-source benchmarking framework, hindering systematic method development and fair evaluation. To address this, we introduce the first modular Python library dedicated to NA, integrating 18 real-world and synthetic datasets, 14 state-of-the-art algorithms, and a standardized evaluation pipeline. Our key contributions are: (i) the first plug-and-play, unified benchmark framework supporting multidimensional joint evaluation—including Top-K accuracy, runtime, memory footprint, and noise robustness; and (ii) an object-oriented, modular API design that significantly enhances reproducibility and extensibility. Comprehensive experiments systematically characterize method performance across scalability, noise perturbation, and cross-domain settings, revealing practical applicability boundaries and empirical guidelines. This work fills a critical gap by providing the first holistic, open, and rigorously designed benchmarking infrastructure for NA research.

Technology Category

• Search and Optimization: Algorithm Configuration
• Machine Learning: Evaluation and Analysis
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

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.

Problem

• Lack of comprehensive library for network alignment benchmarking
• Need standardized evaluation for network alignment methods
• Limited tools for systematic NA method development

Innovation

• Python library for network alignment benchmarking
• Integrates 18 datasets and 14 methods
• Standardized evaluation with diverse metrics