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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 89-101    DOI: 10.11925/infotech.2096-3467.2021.0544
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Discovering Potentialities of User Ideas from Open Innovation Communities with Graph Attention Network
Wang Song1,Yang Yang1(),Liu Xinmin1,2
1College of Economics and Management, Shandong University of Science and Technology, Qingdao 266590, China
2Qingdao Agricultural University, Qingdao 266109, China
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Abstract  

[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.

Key wordsOpen Innovation Community      Dual Network Structure Model      Graph Attention Network      Potential Value Discovery     
Received: 01 June 2021      Published: 23 December 2021
ZTFLH:  G206  
Fund:National Natural Science Foundation of China(71471105);Social Science Planning Fund Program of Sahndong Province(18CGLJ38)
Corresponding Authors: Yang Yang,ORCID:0000-0003-1967-9781     E-mail: 17863907712@163.com

Cite this article:

Wang Song, Yang Yang, Liu Xinmin. Discovering Potentialities of User Ideas from Open Innovation Communities with Graph Attention Network. Data Analysis and Knowledge Discovery, 2021, 5(11): 89-101.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0544     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2021/V5/I11/89

The Dual Network Structure Model of User Ideas
所属网络 名称 符号 含义
用户社交网络 用户专业性 professionalism 用户节点的专业程度
度中心性 degree 与其他用户节点的连接数量
中间中心性 betweenness 用户节点位置的重要性
接近中心性 closeness 用户节点到其他节点用户的距离
用户领袖性 pagerank 用户节点的影响力
内容知识网络 全息性 holographic 创意内容可拓展的信息量,全面程度
丰富性 richness 呈现创意的多种形式
情感极性 emotionality 创意带有的情感倾向
语义关联性 adjacency 创意核心语义关联的复杂程度
The Dual Network Structure Characteristics for User Ideas
The Potential Value of User Ideas Discovery Model Structure Based on GAT
符号 含义 符号 含义
post_id 创意编号 post_if_has_pic 创意是否有图片
post_theme 创意主题 post_content 创意内容
post_author_id 发表创意的用户编号 post_author_name 发表创意的用户昵称
post_reputation 发表创意的用户声望 review_author_id 评论用户编号
review_author_name 评论用户昵称 review_content 评论内容
The Basic Data Characteristics
The Dual Network Structure Model Diagram of User Ideas in Meizu Community
The Number of Topics of Creative Value-confusion Degree Graph
The Training Results of Early Identification Model for User Ideas Potential Value in Meizu Community
输入 准确率 误差
用户创意双重网络特征 90.49% 0.882 8
内容知识网络特征 79.31% 0.909 0
用户社交网络特征 60.00% 1.194 7
The Results for Different Inputs
模型 准确率
图注意力模型 90.49%
图神经网络 78.64%
BP神经网络 35.25%
CNN神经网络 45.68%
支持向量机 34.58%
随机森林 34.24%
The Experimental Results
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