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Research paper | Open Access

Crowd intelligence evolution based on complex network

Jianran Liu1, Wen Ji2
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China and University of Chinese Academy of Sciences, Beijing, China
Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Abstract

Purpose

In recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks.

Design/methodology/approach

This paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence.

Findings

The authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout.

Practical implications

The simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed.

Originality/value

Based on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.

References

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International Journal of Crowd Science
Pages 281-292

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Cite this article:
Liu J, Ji W. Crowd intelligence evolution based on complex network. International Journal of Crowd Science, 2021, 5(3): 281-292. https://doi.org/10.1108/IJCS-03-2021-0008

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Received: 03 March 2021
Revised: 27 August 2021
Accepted: 01 September 2021
Published: 07 October 2021
© The author(s)

Jianran Liu and Wen Ji. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode