Abstract
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.