Can I read K-layer for Influencer Identification in Complex Networks on EtoBox?

K-layer for Influencer Identification in Complex Networks by Yuecheng Cai; Wen Zhou is a Physics and Astronomy article available to read on EtoBox.

What is K-layer for Influencer Identification in Complex Networks about?

A small set of influential nodes, called influencers, spread information through a network faster and broader than other nodes. Identifying influencers has profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failures. In this paper, we use and leverage the idea behind the widely-used k-shell index to introduce a new centrality index we call k-layer. The k-layer index, calculated through k-layer decomposition, quantifies the core of the network through the distance of nodes from the periphery of the network. Intuitively, the k-layer value of node i represents the depth of the node tree with node i as the root node within the scope of nodes that have been already removed. Our experimental results show that the proposed k-layer metric outperforms the k-shell index and the Mixed Degree Decomposition(MDD) in detecting the influencers of networks. After that, taking into account the node location characteristics in the network, an extended k-layer index, named KR-layer (KLR) is proposed and proved to have better ability to identify key nodes in complex networks. Our findings reveal the essential role of nodes' distance

Who reads K-layer for Influencer Identification in Complex Networks?

Author

Yuecheng Cai; Wen Zhou

Publisher

IOP Publishing

Published

2020

Language

EN

Field

Physics and Astronomy (Physical Sciences)