A Decentralized Classification Algorithm for Online Consumers Based on Improved LPA

doi:10.11925/infotech.2096-3467.2022.0795

Data Analysis and Knowledge Discovery

2023, Vol. 7

Issue (10): 25-36 DOI: 10.11925/infotech.2096-3467.2022.0795

Current Issue | Archive | Adv Search

A Decentralized Classification Algorithm for Online Consumers Based on Improved LPA

Liu Zhu¹,Qian Xiaodong²(

)

¹School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
²School of Economics & Management, Lanzhou Jiaotong University, Lanzhou 730070, China

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Abstract Abstract: [Objective] This paper proposes a classification algorithm based on the improved LPA model,aiming to improve the label propagation distance and node similarity judgment in decentralized e-commerce network consumer classification. [Methods] Firstly, we introduced the cosine similarity formula to measure the similarity of nodes and constructed a similarity adjacency matrix. These steps improved the measurement of node distance according to their shared relationship in the LPA algorithm. We also introduced the principle of a back lookup table to conform to the characteristics of locality and reduce the time complexity. Secondly, we selected the initial center point with the degree centrality index and used the clustering coefficient index to update the label rules. We proposed the label propagation distance optimization formula to make the LPA algorithm meet the locality requirements. [Results] The category structure modularity Q of the improved LPA algorithm was 0.054 and 0.145 higher than the traditional LPA algorithm in the network with two neighbor similarity thresholds. The modular Q value increased up to 0.092 on data of different scales. [Limitations] The paper needs to set two parameters and use the principle of the back lookup table. The relationship between time complexity and network size is square. [Conclusions] The improved LPA can more effectively limit label propagation, which creates higher intra-categories node similarity and lower inter-categories node similarity. It is suitable for analyzing decentralized e-commerce consumer networks.

Key words： Decentralization E-Commerce Consumer Network Locality Tag Propagation Classification

Received: 31 July 2022 Published: 22 March 2023

ZTFLH:	TP393
	G350

Fund:National Natural Science Foundation of China(71461017)

Corresponding Authors: Qian Xiaodong，ORCID：0000-0001-6425-7559，E-mail：qianxd@mail.lzjtu.cn。

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Cite this article:

Liu Zhu, Qian Xiaodong. A Decentralized Classification Algorithm for Online Consumers Based on Improved LPA. Data Analysis and Knowledge Discovery, 2023, 7(10): 25-36.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0795 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I10/25

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$ρ$ and $λ$ Relation Diagram

“Barbell Network” Topology

Schematic Diagram of Dividing “Barbell Network” Category by This Algorithm

Schematic Diagram of LPA Dividing “Barbell Network” Category

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Network Topology（ $ψ$ = 0.95）

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Network Topology （ $ψ$ = 0.90）

算法	$Q$
算法	$ψ = 0.95$	$ψ = 0.90$
LPA	0.629	0.478
本文算法	0.683	0.623

Test Results of Real Network

Q

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Network Modularity of Different Scales

Q Contribution Value Analysis（

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$Q$ Contribution Value Analysis（ $ψ = 0.95$ ）

Q Contribution Value Analysis（

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$Q$ Contribution Value Analysis（ $ψ = 0.90$ ）

Convergence Analysis of Experimental Results

Types of Consumer Groups

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