Community overlap discovery algorithm based on industrial big data

被引：0
|
作者：
Kang H. [1 ]
Jing W. [1 ]
Zhang Y. [1 ]
机构：
[1] School of Information Management, Beijing Information Science and Technology University, Beijing
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2024年 / 30卷 / 06期
关键词：
community detection; industrial big data; label propagation; overlapping community; random walk;
D O I：
10.13196/j.cims.2021.0791
中图分类号：
学科分类号：
摘要：
Industrial big data has a large scale, complex structure, and high value density. To deeply explore and analyze its hidden relationships, trends and patterns, and to provide better decision-making basis for enterprises, combined with the idea of random walk and label propagation, a community overlap discovery algorithm based on industriai big data was proposed. The algorithm of seed node selection was designed, the importance of each node was calculated by random walk, and the irrelevant and important seed nodes were selected. Then, an overlapping community discovery algorithm was proposed, the seed node was given a unique label, and the label was propagated iteratively until the node label was no longer changed. The final overlapping community division result was obtained according to the node label, finally, comparative experiments were carried out on real data sets and artificial data sets, the results showed that the algorithm could effectively find high-quality overlapping communities on the network. The algorithm could be applied to data analysis and information mining of industrial big data. © 2024 CIMS. All rights reserved.
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页码：2130 / 2138
页数：8
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