基于图形神经网络的云制造服务推研究
Minghui Li1, Xiaoqiu Shi1,2, Yuqiang Shi1
1School of Manufacturing Science and Engineering, Southwest University of Science and Technology, Mianyang, China.
PloS one
|September 26, 2023
概括
本研究引入了一个图形神经网络方法来推云制造 (CMfg) 服务,克服信息过载. 该方法通过提高链接预测准确度来增强资源发现.
科学领域:
- 计算机科学 计算机科学
- 制造业 工程 制造工程
- 人工智能的人工智能
背景情况:
- 由于越来越多的服务资源,云制造 (CMfg) 平台面临信息过载.
- 传统的推方法很难有效地确定合适的CMfg资源.
研究的目的:
- 为CMfg服务资源提出基于图形神经网络 (GNN) 的新型推方法.
- 为解决企业寻求CMfg服务的信息过载问题.
主要方法:
- 在CMfg服务特征上使用相似度量 (Cosine,Pearson) 构建资源图数据集.
- 应用GNN用于表示学习以获得资源的矢量嵌入.
- 通过对节点嵌入的点产品计算来预测潜在的资源链接,以提供建议.
主要成果:
- 与现有的算法相比,拟议的GNN方法显示出更高的链接预测准确性.
- 在训练期间网络密度较高时,预测性能显著提高.
结论:
- 基于GNN的方法有效地推合适的CMfg服务资源.
- 这种方法为导航复杂的CMfg服务平台提供了可行的解决方案.
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