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Tsinghua Science and Technology 2023, 28(4): 729-742 https://doi.org/10.26599/TST.2022.9010021
Open Access | Issue | Published: 06 January 2023

Node Search Contributions Based Long-Term Follow-Up Specific Individual Searching Model

Show Author's Information Yayong Shi1,†,  Fei Chang1,†,  Yetao Sun1,  Guangcheng Yang1,  Rui Wang1( ),  Yuan Yao2( )
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100183, China
Institute for Hospital Management Research, Chinese PLA General Hospital, Beijing 100853, China

Yayong Shi and Fei Chang contributed equally to this work.

Keywords:
complex networks, specific individual search, propagation dynamics, long-term follow-up
Cite this article:
Shi Y, Chang F, Sun Y, et al. Node Search Contributions Based Long-Term Follow-Up Specific Individual Searching Model. Tsinghua Science and Technology, 2023, 28(4): 729-742. https://doi.org/10.26599/TST.2022.9010021
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Abstract Full text About this article

In this paper, we introduce a long-term follow-up specific individual searching (SIS) model. This model introduces the concept of node search contributions by considering the characteristics of the network structure. A node search contribution indicates the ability of a certain node to correctly guide the search path and successfully complete an SIS. The influencing factors of node search contributions have three components: the individual influence index, attribute similarity, and node search willingness. On the basis of node search contributions and the PeopleRank idea, this paper proposes an SIS model based on node search contribution values and conducts comparison experiments with several mainstream SIS algorithms in three aspects: the search failure rate, the minimum number of search hops, and the search size. The experimental results verify the advanced nature and operability of the model proposed in this paper, which presents theoretical and practical significance to the quantitative study of the SIS process.


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About this article

Node Search Contributions Based Long-Term Follow-Up Specific Individual Searching Model

Show Author's information Yayong Shi1,†, Fei Chang1,†, Yetao Sun1, Guangcheng Yang1, Rui Wang1( ), Yuan Yao2( )
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100183, China
Institute for Hospital Management Research, Chinese PLA General Hospital, Beijing 100853, China

Yayong Shi and Fei Chang contributed equally to this work.

Abstract

In this paper, we introduce a long-term follow-up specific individual searching (SIS) model. This model introduces the concept of node search contributions by considering the characteristics of the network structure. A node search contribution indicates the ability of a certain node to correctly guide the search path and successfully complete an SIS. The influencing factors of node search contributions have three components: the individual influence index, attribute similarity, and node search willingness. On the basis of node search contributions and the PeopleRank idea, this paper proposes an SIS model based on node search contribution values and conducts comparison experiments with several mainstream SIS algorithms in three aspects: the search failure rate, the minimum number of search hops, and the search size. The experimental results verify the advanced nature and operability of the model proposed in this paper, which presents theoretical and practical significance to the quantitative study of the SIS process.

Keywords: complex networks, specific individual search, propagation dynamics, long-term follow-up

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Publication history
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Publication history

Received: 22 January 2022
Accepted: 16 June 2022
Published: 06 January 2023
Issue date: August 2023

Copyright

© The author(s) 2023.

Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2016YFC0901303), and the National Natural Science Foundation of China (Nos. 72004147 and 62173158).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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