In complex network analysis, the problem of ranking individual nodes based on their importance has attracted increasing attention from the scientific community due to its vast application, such as identification of influential spreaders for viral marketing or epidemic control, bottlenecks for traffic congestion control, and so on. The growing literature proposes a number of measures to determine the rank order of the network entities where complete information about the nodes and their interaction is available. Degree centrality, PageRank, eigenvector centrality, closeness centrality are few such popular measures. In most real-life scenarios, however, the information about the underlying network is incomplete or affected due to noise. The few works that study the effects of incomplete information on the rank orders show the vulnerability of the rank orders in various topologies. In this paper, we investigate the effects of noise, both random and nonrandom, on the aggregated rank orders determined from the degree, PageRank, eigenvector centrality, and closeness centrality-based rankings. This paper reveals an important insight that even the simple Borda Count ranking has the potential to improve on the accuracy of rank orders in networks with uncertainty. This paper shows the existence of stable nodes in various networks and indicates that the design of the consensus approach based on the properties of the stable nodes can further improve the stability of the rank orders.