Product Competition Prediction in Engineering Design Using Graph Neural Networks
Product Competition Prediction in Engineering Design Using Graph Neural Networks
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DOI：
10.1115/1.4054299
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发表时间：
2022-01
期刊：
影响因子：
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通讯作者：
Faez Ahmed;Yaxin Cui;Yan Fu;Wei Chen
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文献类型：
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作者：
Faez Ahmed;Yaxin Cui;Yan Fu;Wei Chen
了解市场体系中不同产品之间的关系，并预测设计的变化如何影响它们的市场地位，可以帮助公司创造更好的产品。提出了一种基于图神经网络的产品间关系建模方法，网络中的节点表示产品，边表示产品之间的关系。我们的模型使我们能够以一种系统的方式预测未来几年看不见的产品之间的关系联系。当应用于中国汽车市场的案例研究时，我们基于归纳图神经网络的方法GraphSAGE的链接预测性能是现有网络建模方法-基于指数随机图模型的汽车共同考虑关系预测方法的两倍。我们的工作还克服了传统网络建模方法的可扩展性和与多种数据类型相关的限制，通过对大量属性、类别和数值混合属性以及未知产品进行建模。虽然普通的GraphSAGE需要一个局部网络来进行预测，但我们使用了“邻接预测模型”来增强它，以绕过需要邻居信息的限制。最后，我们展示了从基于排列的可解释性分析中获得的见解如何帮助制造商理解设计属性如何影响产品关系的预测。总体而言，这项工作提供了一种系统的数据驱动方法来预测汽车市场等复杂网络中产品之间的关系。
Understanding relationships between different products in a market system and predicting how changes in design impact their market position can be instrumental for companies to create better products. We propose a graph neural network-based method for modeling relationships between products, where nodes in a network represent products and edges represent their relationships. Our modeling enables a systematic way to predict the relationship links between unseen products for future years. When applied to a Chinese car market case study, our method based on an inductive graph neural network approach, GraphSAGE, yields double the link prediction performance compared to an existing network modeling method—exponential random graph model-based method for predicting the car co-consideration relationships. Our work also overcomes scalability and multiple data type-related limitations of the traditional network modeling methods by modeling a larger number of attributes, mixed categorical and numerical attributes, and unseen products. While a vanilla GraphSAGE requires a partial network to make predictions, we augment it with an “adjacency prediction model” to circumvent the limitation of needing neighborhood information. Finally, we demonstrate how insights obtained from a permutation-based interpretability analysis can help a manufacturer understand how design attributes impact the predictions of product relationships. Overall, this work provides a systematic data-driven method to predict the relationships between products in a complex network such as the car market.