Almost all the complex interactions between humans, animals, biological cells, neurons, or any other objects are now modeled as a graph with the nodes as the objects of interest and interactions as the edges. The identification of the most central or influential node in such a complex network has many practical applications in diverse domains such as viral marketing, infectious disease spreading, rumor spreading in a social network, virus/worm spreading in computer networks, etc. Many centrality measures using the position/location of a node and network structure have been proposed in the literature. The node degree, shortest paths(closeness), and betweenness are used since long with degree capturing local effect and others global effect. The k-shell considers the coreness of the nodes by dividing the network into layers or shells. Many variations of k-shell proposed in recent years, as well as many researchers, use k-shell as a building block in their heuristic technique to alleviate the problems of classical k-shell and to identify influential spreaders more elegantly. The main objective of this paper is to analyze and compare the major variations of the k-shell based methods along with representative network topology based hybrid techniques by considering a toy network with detailed computations. A discussion on different performance evaluation metrics and, simulation models such as the SIR epidemic model, has been undertaken with a comparative analysis between different state-of-the-art on a few standard real networks. © 2020 Elsevier Ltd