Network backbone extraction is essential for simplifying complex systems while preserving key structural characteristics. Traditional methods often compromise important properties like edge weight distribution. This study introduces an innovative approach by applying global similarity-based link prediction techniques to extract network backbones. Through a comparative analysis using the world air transportation network, we reveal how different methods reflect various aspects of the hub-and-spoke model employed by airlines. Specifically, the Katz backbone method effectively combines local and global connections, while the High Salience Skeleton method excels in maintaining network nodes and reachability. Based on three real-world networks, our results show that the Katz backbone preserves weight distribution most effectively, whereas the SP and Disparity backbones maintain degree distribution more accurately. These findings suggest that the backbone extraction technique should be tailored to the specific network property of interest.