Key aspects of network alignment documentation.

Network alignment (NA) is a computational methodology employed to compare biological networks across different species or conditions. By identifying conserved structures, functions, and interactions, NA provides invaluable insights into shared biological processes, evolutionary relationships, and system-level behaviors. This manuscript presents a comprehensive overview of NA methodologies, including the importance of preprocessing network data, selecting suitable input formats, and understanding diverse network types such as attributed, temporal, and multilayer networks. Additionally, it explores key challenges such as seed nodes selection, algorithm configuration, and cross-species alignment, emphasizing the necessity of integrating functional annotations, sequence similarity, and network topology for biologically meaningful results. Various NA strategies, including Local and Global Network Alignment, are discussed alongside their respective advantages and limitations. Practical recommendations for effectively documenting and visualizing NA experiments are also provided, ensuring reproducibility and clarity in research. By leveraging diverse alignment tools and adopting best practices, researchers can unlock the potential of NA to advance our understanding of complex biological systems.网络比对（Network Alignment，NA）是一类用于跨不同物种或实验条件比对生物网络的计算方法学。通过识别保守的结构、功能与相互作用，网络比对能够为共享生物过程、进化关系以及系统级行为提供极具价值的研究视角。本文对网络比对方法学进行了全面综述，涵盖网络数据预处理的重要性、合适输入格式的选择，以及对属性网络、时序网络、多层网络等多样网络类型的认知。此外，本文探讨了种子节点选择、算法配置、跨物种比对等核心挑战，并强调为获得具备生物学意义的结果，需整合功能注释、序列相似性与网络拓扑结构。本文还讨论了包括局部网络比对、全局网络比对在内的多种网络比对策略，并分析了各类策略各自的优势与局限性。同时，本文给出了有效记录与可视化网络比对实验的实操建议，以保障研究的可重复性与结果清晰度。研究人员通过运用多样化的比对工具并遵循最佳实践，可充分发挥网络比对的潜力，从而推动我们对复杂生物系统的认知进阶。