Group-based strategy diffusion in multiplex networks with weighted values

Yu, Jianyong; Jiang, Jiuchuan; Xiang, Leijun

doi:10.1016/j.physa.2016.11.050

Cited by 4 publications

(1 citation statement)

References 27 publications

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“…Pósfai et al (2016) use maximum flows to study the controllability of 2-layer multiplex networks. Very recently Yu et al (2017) proposed an analysis of the diffusion dynamics of binary states (such as the Prisoner’s Dilemma) in a multiplex social networks, investigating how different community layers influence this diffusion.…”

Section: Related Workmentioning

confidence: 99%

Multiplex flows in citation networks

2017

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Knowledge is created and transmitted through generations, and innovation is often seen as a process generated from collective intelligence. There is rising interest in studying how innovation emerges from the blending of accumulated knowledge, and from which path an innovation mostly inherits. A citation network can be seen as a perfect example of one generative process leading to innovation. However, the impact and influence of scientific publication are always difficult to capture and measure. We offer a new take on investigating how the knowledge circulates and is transmitted, inspired by the notion of “stream of knowledge”. We propose to look at this question under the lens of flows in directed acyclic graphs (DAGs). In this framework inspired by the work of Strahler, we can also account for other well known measures of influence such as the h-index. We propose then to analyze flows of influence in a citation networks as an ascending flow. From this point on, we can take a finer look at the diffusion of knowledge through the lens of a multiplex network. In this network, each citation of a specific work constitutes one layer of interaction. Within our framework, we design three measures of multiplex flows in DAGs, namely the aggregated, sum and selective flow, to better understand how citations are influenced. We conduct our experiments with the arXiv HEP-Th dataset, and find insights through the visualization of these multiplex networks.

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Section: Related Workmentioning

confidence: 99%