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Open Access

A Crowd Equivalence-Based Massive Member Model Generation Method for Crowd Science Simulations

Aoqiang Xing^¹, Hongbo Sun^¹()

1 School of Computer and Control Engineering, Yantai University, Yantai 264005, China

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Abstract

Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As model-based scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. The member model generation method based on crowd equivalence is verified through simulations. A simulation tool is developed to generate massive member models to support crowd science simulations and crowd science studies.

Keywords

simulation member model generation crowd equivalence crowd network

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International Journal of Crowd Science

Volume 6 Issue 1,
April 2022

Pages 23-33

DOI: 10.26599/IJCS.2022.9100004

Cite this article:

Xing A, Sun H. A Crowd Equivalence-Based Massive Member Model Generation Method for Crowd Science Simulations. International Journal of Crowd Science, 2022, 6(1): 23-33. https://doi.org/10.26599/IJCS.2022.9100004

Algorithm 1 Community assignment node
Input: nodeList; communityList
Output: communitynodeList
1 　sort the node list in descending order of node degree, create a community node list
2 　 for each ${c o m m u n i t y}_{i}$ in communityList do
3 　 for each ${n o d e}_{i}$ in nodeList do
4 　make d = communityDegree
5 　 if node degree $d_{i} < d$ , then
6 　　 if the number of community nodes + 1 < community scale s, then
7 　add this node
8 　　 else if the number of community nodes + 1 = community scale s, then
9 　　determine the degree of the community node list and the position of the first node in the node list
10 　 if the community degree sum > the degree sum of the current array + the current node degree, then
11 　clear the community node list, filter from the next node in the node list where the first node is located, return 4
12 　　 else the current node joins the community node list
13 　 end if
14 　 end if
15 　 end
16 　 else if node degree $d_{i} = d$ , then
17 　　 if the number of community nodes + 1 < community scale s, then
18 　　abandon this node
19 　　 else if the number of community nodes + 1 = community scale s, then
20 　add this node
21 　 end if
22 　 else if node degree $d_{i} > d$ , then
23 　abandon this node
24 　 end if
25 　 end