Abstract
For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. K-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. K-shell method requires the entire network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In the present work, first, we show that the shell-index of a node can be estimated using its \(h^2-index\) which can be computed using local neighborhood information. We further show that \(h^2-index\) has better monotonicity and correlation with the spreading power of the node than the shell-index. Next, we propose hill-climbing based methods to identify top-ranked nodes in a small number of steps. We further propose a heuristic method to estimate the percentile rank of a node without computing influential power of all the nodes.
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Saxena, A., Iyengar, S.R.S. (2018). K-Shell Rank Analysis Using Local Information. In: Chen, X., Sen, A., Li, W., Thai, M. (eds) Computational Data and Social Networks. CSoNet 2018. Lecture Notes in Computer Science(), vol 11280. Springer, Cham. https://doi.org/10.1007/978-3-030-04648-4_17
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