Ranking spreaders by decomposing complex networks

By An Zeng, Cheng-Jun Zhang. arXiv.org, 2012

407 citations recorded by the source corpus.

Abstract Ranking the nodesʼ ability of spreading in networks is crucial for designing efficient strategies to hinder spreading in the case of diseases or accelerate spreading in the case of information dissemination. In the well-known k-shell method, nodes are ranked only according to the links between the remaining nodes (residual links) while the links connecting to the removed nodes (exhausted links) are entirely ignored. In this Letter, we propose a mixed degree decomposition (MDD) procedure in which both the residual degree and the exhausted degree are considered. By simulating the epidemic spreading process on real networks, we show that the MDD method can outperform the k-shell and degree methods in ranking spreaders.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/ffaccef77df75a50103c911b56dd79aa2828895f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Study on centrality measures in social networks: a survey (2018, background): “Zeng and Zhang (2013) proposed a method called mixed degree decomposition by taking residual degree and also exhausted degree.”
Identifying influential spreaders in complex networks based on gravity formula (2015, methodology): “proposed a mixed degree decomposition method by incorporating the residual degree and the exhausted degree [17]; Chen et al.”
Vital nodes identification in complex networks (2016, background): “As an index for nodes’ influences, coreness can be applied to large-scale network easily for its low computational complexity, and thus has found applications in many real networks [37, 58, 59].”