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Research paper | Open Access

Crowd intelligence evolution based on complex network

Jianran Liu^¹, Wen Ji^²

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.

Keywords

Crowd intelligence Intelligence evolution Complex network Agent interaction Force layout

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International Journal of Crowd Science

Volume 5 Issue 3,
November 2021

Pages 281-292

DOI: 10.1108/IJCS-03-2021-0008

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