  In real world complex networks, the importance of a node depends on two
important parameters: 1. characteristics of the node, and 2. the context of the
given application. The current literature contains several centrality measures
that have been defined to measure the importance of a node based on the given
application requirements. These centrality measures assign a centrality value
to each node that denotes its importance index. But in real life applications,
we are more interested in the relative importance of the node that can be
measured using its centrality rank based on the given centrality measure. To
compute the centrality rank of a node, we need to compute the centrality value
of all the nodes and compare them to get the rank. This process requires the
entire network. So, it is not feasible for real-life applications due to the
large size and dynamic nature of real world networks. In the present project,
we aim to propose fast and efficient methods to estimate the global centrality
rank of a node without computing the centrality value of all nodes. These
methods can be further extended to estimate the rank without having the entire
network. The proposed methods use the structural behavior of centrality
measures, sampling techniques, or the machine learning models. In this work, we
also discuss how to apply these methods for degree and closeness centrality
rank estimation.
