contributor author | Ahmed, Faez;Cui, Yaxin;Fu, Yan;Chen, Wei | |
date accessioned | 2022-12-27T23:11:23Z | |
date available | 2022-12-27T23:11:23Z | |
date copyright | 5/9/2022 12:00:00 AM | |
date issued | 2022 | |
identifier issn | 2770-3495 | |
identifier other | aoje_1_011020.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4288063 | |
description abstract | 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Product Competition Prediction in Engineering Design Using Graph Neural Networks | |
type | Journal Paper | |
journal volume | 1 | |
journal title | ASME Open Journal of Engineering | |
identifier doi | 10.1115/1.4054299 | |
journal fristpage | 11020 | |
journal lastpage | 11020_12 | |
page | 12 | |
tree | ASME Open Journal of Engineering:;2022:;volume( 001 ) | |
contenttype | Fulltext | |