Discovering Potentialities of User Ideas from Open Innovation Communities with Graph Attention Network

[Objective] This paper proposes a method to discover the potentialities of user ideas, aiming to effectively identify creative ones from open innovation communities. [Methods] First, we analyzed the formation process of creative value and constructed the dual network structure for user ideas. Then, we developed a model based on graph attention networks to discover their potential values. Third, we trained the model to learn the node characteristics of this dual network and mapped the relationships between networks. [Results] The model was empirically examined with data from a typical open innovation community. The results show that the proposed model achieved an accuracy rate of 90.49%, higher than other relevant baseline models. [Limitations] The model was only validated on the Meizu community dataset, which needs to be expanded to other open innovation communities in future studies. [Conclusions] The combination of the dual network structure and the graph attention network can effectively identify the potential value of user ideas in the open innovation community, which provides technical support for increasing user participation and fully utilizes the community innovation resources. © 2021, Chinese Academy of Sciences. All rights reserved.