Link Influence Entropy

Priti Singh, Abhishek Chakraborty and B.S. Manoj

Physica A: Statistical Mechanics and its Applications, 2017, vol. 465, issue C, 701-713

Abstract: In this paper we propose a new metric, Link Influence Entropy (LInE), which describes importance of each node based on the influence of each link present in a network. Influence of a link can neither be effectively estimated using betweenness centrality nor using degree based probability measures. The proposed LInE metric which provides an effective way to estimate the influence of a link in the network and incorporates this influence to identify nodal characteristics, performs better compared to degree based entropy. We found that LInE can differentiate various network types which degree-based or betweenness centrality based node influence metrics cannot. Our findings show that spatial wireless networks and regular grid networks, respectively, have lowest and highest LInE values. Finally, performance analysis of LInE is carried out on a real-world network as well as on a wireless mesh network testbed to study the influence of our metric as well as influence stability of nodes in dynamic networks.

Keywords: Complex networks; Network entropy; Average path length; LInE; Influence stability; Wireless mesh networks (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:465:y:2017:i:c:p:701-713

DOI: 10.1016/j.physa.2016.08.019

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