Optimal design of hydrometric networks has been a long-standing problem in hydrology. Evaluation of the importance (or influence) of the individual monitoring stations is key to achieve an optimal design of a hydrometric network. The present study employs the concepts of complex networks towards assessing the importance of individual stations in a hydrometric network. For implementation, a streamflow network of 218 stations in Australia is studied, and monthly streamflow data of 26 years (1981–2006) are analyzed. Each station is considered as a node in the network and the connections between any pair of nodes are identified based on mutual information in the streamflow values. Six different node ranking measures are used to examine the importance of nodes in the network: degree centrality, betweenness centrality, closeness centrality, degree and influence of line, weighted degree betweenness, and clustering coefficient. Different threshold values of mutual information are also considered to examine the influence of threshold on the best node ranking measure. The six node ranking measures are evaluated using the decline rate of network efficiency. The results indicate that different node ranking measures identify different stations as the most important and least important in the network. Betweenness centrality and weighted degree betweenness generally perform the best in identifying the most important stations across the thresholds. The weighted degree betweenness measure outperforms the others in the identification of the least important stations, especially at higher thresholds. The clustering coefficient performs the worst in identifying the importance of stations in the streamflow monitoring network.