2018/05/25 by Akrati Saxena, Saxena, Akrati, S. R. S. Iyengar +1 · 1 citation
Computer Science · Engineering · Mathematics · Physics and Astronomy · Social Sciences · #Combinatorics #Complex Network Analysis Techniques #Computer science #Data mining #Engineering #Estimator #FOS: Computer and information sciences #FOS: Physical sciences #Graph #Graph theory and applications #Human Mobility and Location-Based Analysis #Index (typography) #Mathematics #Node (physics) #Physics and Society (physics.soc-ph) #Rank (graph theory) #Shell (structure) #Social and Information Networks (cs.SI) #Statistics #Theoretical computer science #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1805.10391
published in arXiv (Cornell University) (Cornell University) · The preliminary version of this paper is submitted in ASONAM 2018(Ph.D. Forum track) and it is under review
openalex publication_date 2018/05/25 · arxiv created 2018/11/23 · arxiv updated 2018/11/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
For network scientists, it has always been an interesting problem to identify the influential nodes in a given network. The k-shell decomposition method is a widely used method which assigns a shell-index value to each node based on its influential power. The k-shell method requires the global information of the network to compute the shell-index of a node that is infeasible for large-scale real-world dynamic networks. In this work, we propose a method to estimate the shell-index of a node using its local information. We also propose hill-climbing based approach to hit the top-ranked nodes in a small number of steps. We further discuss a method to estimate the rank of a node based on the proposed estimator.