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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.
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Received: 31 July 2022
Published: 22 March 2023
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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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