Identification of influential nodes in complex networks using innovative centrality measures a comparative study of hiks and nnp entropy centrality

Abstract

Complex networks are fundamental frameworks that enable the understanding of the intricate interrelationships prevalent in a multitude of real-world systems, including social interactions, biological phenomena, technological infrastructures, and ecological ecosystems. These networks demonstrate emergent properties stemming from the interactions and dependencies among their constituent nodes, rendering them indispensable tools for deciphering the dynamics of complex systems. At the crux of unraveling the complexities inherent in these networks lies the crucial task of identifying influential nodes entities whose actions or attributes wield substantial influence over network dynamics. This thesis undertakes the challenging task of identifying influential nodes within complex networks, a pursuit deemed essential for endeavors such as rumor containment, analysis of virus spread dynamics, and the formulation of effective viral marketing strategies. Despite concerted research efforts, the challenge of identifying influential nodes with minimal computational complexity while also considering the nuanced relationships between nodes persists. In response to this challenge, this thesis proposes a novel methodology that integrates optimal community detection with hybrid K-shell decomposition techniques, aiming to tackle this complex task effectively. newline