Identification of influential spreaders plays a significant role in maximizing or controlling any spreading process in a network. Influential spreaders or influential nodes are the most important nodes in a network that play a key role in spreading information, ideas, or diseases. To identify and rank these important nodes many centrality measures have been proposed by several authors over the past few years. Identifying the major research gap, we observe measuring the node importance in dynamic networks is very limited. Dynamic networks are defined as those networks which change the connectivity structure of the network at every time interval, also known as a time-varying network, as the links are active only at certain points in time. In dynamic networks, ranking the nodes (or finding the most important nodes) becomes computation-heavy if we have to compute the centrality measures on every event (changes in the connectedness of the network). We have considered two easy-to-compute and well-established centrality metrics (K-shell and degree) and attempted to estimate the node ranking by approximating the centrality measures through partial computation for a network that has undergone some changes in the topology (the edge connectivity, some edges become dormant and some new connections between two nodes might have formed). Our primary objective is to avoid computing centrality measures every time, during the changes of connectivity change. We utilize the existing network measures and other parameters along with the changes of connectivity structure to arrive at the updated measures as well as ranking heuristically. It is expected that after every event the topology of the network changes, which in turn would change centrality measures. The focus of the present study is to find out without further re-computation from the beginning the changes in K-shell centrality and degree centrality measures on a particular network model after some changes occur. This proposed method will heuristically estimate the changed k shell values using partial computation.