Please wait a minute...
Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (11): 89-101    DOI: 10.11925/infotech.2096-3467.2021.0544
Current Issue | Archive | Adv Search |
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
Download: PDF (1800 KB)   HTML ( 2
Export: BibTeX | EndNote (RIS)      

[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:

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:     OR

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
[1] Von Briel F, Recker J. Lessons from a Failed Implementation of an Online Open Innovation Community in an Innovative Organization[J]. MIS Quarterly Executive, 2017, 16(1):35-46.
[2] 胡媛, 韦肖莹, 王灿. 微博信息质量评价指标体系构建研究[J]. 情报科学, 2017, 35(6):44-50.
[2] (Hu Yuan, Wei Xiaoying, Wang Can. Research on the Construction of Micro-blog Information Quality Evaluation Indicator System[J]. Information Science, 2017, 35(6):44-50.)
[3] 易明, 张婷婷, 李梓奇. 多维特征下社会化问答社区答案排序研究[J]. 图书情报工作, 2020, 64(17):103-113.
[3] (Yi Ming, Zhang Tingting, Li Ziqi. Research on the Ranking of Social Q&A Community Answers Based on Multidimensional Features[J]. Library and Information Service, 2020, 64(17):103-113.)
[4] 易明, 张婷婷. 大众性问答社区答案质量排序方法研究[J]. 数据分析与知识发现, 2019, 3(6):12-20.
[4] (Yi Ming, Zhang Tingting. Ranking Answer Quality of Popular Q&A Community[J]. Data Analysis and Knowledge Discovery, 2019, 3(6):12-20.)
[5] 吴雅威, 张向先, 陶兴, 等. 基于用户感知的学术问答社区答案质量评价指标构建[J]. 情报科学, 2020, 38(10):141-147.
[5] (Wu Yawei, Zhang Xiangxian, Tao Xing, et al. Construction of Answer Quality Evaluation Index Based on User Perception of Academic Question and Answer Community[J]. Information Science, 2020, 38(10):141-147.)
[6] 祝琳琳, 李贺, 刘金承, 等. 在线评论信息质量感知评价指标体系构建研究[J]. 情报理论与实践, 2021, 44(4):1-15.
[6] (Zhu Linlin, Li He, Liu Jincheng, et al. Research on the Construction of Evaluating Indicator System of Information Quality Perception for Online Reviews[J]. Information Studies: Theory & Application, 2021, 44(4):1-15.)
[7] 单晓红, 王春稳, 刘晓燕, 等. 基于知识网络的开放式创新社区知识发现研究[J]. 复杂系统与复杂性科学, 2020, 17(1):62-70,94.
[7] (Shan Xiaohong, Wang Chunwen, Liu Xiaoyan, et al. Research on Knowledge Discovery of Open Innovation Community Based on Knowledge Network[J]. Complex Systems and Complexity Science, 2020, 17(1):62-70, 94.)
[8] 沈旺, 李世钰, 刘嘉宇, 等. 问答社区回答质量评价体系优化方法研究[J]. 数据分析与知识发现, 2021, 5(2):83-93.
[8] (Shen Wang, Li Shiyu, Liu Jiayu, et al. Research on the Optimization Method of Q&A Community Response Quality Evaluation[J]. Data Analysis and Knowledge Discovery, 2021, 5(2):83-93.)
[9] 郭顺利, 张向先, 陶兴, 等. 社会化问答社区用户生成答案质量自动化评价研究——以“知乎”为例[J]. 图书情报工作, 2019, 63(11):118-130.
[9] (Guo Shunli, Zhang Xiangxian, Tao Xing, et al. Research on Automated Evaluation of User Generated Answer Quality in Social Question and Answer Community - Taking “Zhihu” as an Example[J]. Library and Information Service, 2019, 63(11):118-130.)
[10] Thomas N K, Welling M. Semi-Supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv: 1609.02907.
[11] 洪闯, 李贺, 毛太田. 开放式创新社区用户知识贡献的采纳机理研究[J]. 现代情报, 2020, 40(5):33-40.
[11] (Hong Chuang, Li He, Mao Taitian. Study on the Adoption Mechanism of Knowledge Contribution from Open Innovation Community Users[J]. Journal of Modern Information, 2020, 40(5):33-40.)
[12] 张晓娟, 周学春. 社区治理策略、用户就绪和知识贡献研究:以百度百科虚拟社区为例[J]. 管理评论, 2016, 28(9):72-82.
[12] (Zhang Xiaojuan, Zhou Xuechun. Research on Community Governing Strategy, User Readiness and Knowledge Contribution: Taking Baidu Encyclopedia as an Example[J]. Management Review, 2016, 28(9):72-82.)
[13] 高贝伦, 韦铁. 创意标题对创意实施可能性的影响:基于首因效应与认知心理学视角[J]. 科技管理研究, 2019, 39(11):275-282.
[13] (Gao Beilun, Wei Tie. Impact of Idea Titles on Idea Implementation: Based on Primacy Effect and Cognitive Psychology[J]. Science and Technology Management Research, 2019, 39(11):275-282.)
[14] 李贺, 祝琳琳, 闫敏, 等. 开放式创新社区用户信息有用性识别研究[J]. 数据分析与知识发现, 2018, 2(12):12-22.
[14] (Li He, Zhu Linlin, Yan Min, et al. Identifying Useful Information from Open Innovation Community[J]. Data Analysis and Knowledge Discovery, 2018, 2(12):12-22.)
[15] 沈洪洲, 史俊鹏, 马巧慧. 社会化问答社区回答内容质量影响特征研究——以“知乎”为例[J]. 情报杂志, 2020, 39(10):169-175,202.
[15] (Shen Hongzhou, Shi Junpeng, Ma Qiaohui. Research on Influential Features of the Answer Quality in Social Q&A Community: Taking “Zhihu” as an Example[J]. Journal of Intelligence, 2020, 39(10):169-175, 202.)
[16] 张海涛, 张鑫蕊, 周红磊, 等. 融合用户偏好与内容特征的短视频传播效果评价研究[J]. 图书情报工作, 2020, 64(16):81-91.
[16] (Zhang Haitao, Zhang Xinrui, Zhou Honglei, et al. Research on the Evaluation of Short Video Communication Effect Based on User Preference and Content Characteristics[J]. Library and Information Service, 2020, 64(16):81-91.)
[17] Klein M, Garcia A C B. High-speed Idea Filtering with the Bag of Lemons[J/OL]. SSRN Electronic Journal, 2014.
[18] 金燕, 孙佳佳. 基于用户画像的UGC质量预判模型[J]. 情报理论与实践, 2019, 42(10):77-83.
[18] (Jin Yan, Sun Jiajia. UGC Quality Prediction Model Based on Persona[J]. Information Studies: Theory & Application, 2019, 42(10):77-83.)
[19] 尹鹏博, 潘伟民, 彭成, 等. 基于用户特征分析的微博谣言早期检测研究[J]. 情报杂志, 2020, 39(7):81-86.
[19] (Yin Pengbo, Pan Weimin, Peng Cheng, et al. Research on Early Detection of Weibo Rumors Based on User Characteristics Analysis[J]. Journal of Intelligence, 2020, 39(7):81-86.)
[20] 姜雯, 许鑫, 武高峰. 附加情感特征的在线问答社区信息质量自动化评价[J]. 图书情报工作, 2015, 59(4):100-105.
[20] (Jiang Wen, Xu Xin, Wu Gaofeng. Online Q&A Community Automatically Information Quality Evaluation with Sentiment Feature[J]. Library and Information Service, 2015, 59(4):100-105.)
[21] Sahu T P, Nagwani N K, Verma S. Topical Authoritative Answerer Identification on Q&A Posts Using Supervised Learning in CQA Sites[C]// Proceedings of the 9th Annual ACM India Conference. 2016: 129-132.
[22] 朱益平, 杜海娇, 张佳, 等. 基于RS-BP神经网络的政务微信公众号信息质量评价模型研究[J]. 情报科学, 2021, 39(2):54-61,69.
[22] (Zhu Yiping, Du Haijiao, Zhang Jia, et al. Research on Information Quality Evaluation Model of Government WeChat Public Account Based on RS-BP Neural Networks[J]. Information Science, 2021, 39(2):54-61, 69.)
[23] Yue T, Wang H, Cheng S, et al. Deep Learning Based QoE Evaluation for Internet Video[J]. Neurocomputing, 2020, 386:179-190.
doi: 10.1016/j.neucom.2019.12.082
[24] Li L, He D Q, Jeng W, et al. Answer Quality Characteristics and Prediction on an Academic Q&A Site: A Case Study on ResearchGate[C]// Proceedings of the 24th International Conference on World Wide Web. 2015: 1453-1458.
[25] 王伟, 冀宇强, 王洪伟, 等. 中文问答社区答案质量的评价研究:以知乎为例[J]. 图书情报工作, 2017, 61(22):36-44.
[25] (Wang Wei, Ji Yuqiang, Wang Hongwei, et al. Evaluating Chinese Answers’ Quality in the Community QA System: A Case Study of Zhihu[J]. Library and Information Service, 2017, 61(22):36-44.)
[26] Petar V K, Guillem C, Arantxa C, et al. Graph Attention Networks[OL]. arXiv Preprint, arXiv: 1710.10903v1.
[27] 周欢, 刘嘉, 王欢芳. 基于图神经网络和标签可重叠社区的社会化影视推荐模型[J]. 情报理论与实践, 2021, 44(6):164-170.
[27] (Zhou Huan, Liu Jia, Wang Huanfang. A Social Movie Recommendation Model Based on Graph Neural Network and Tag Overlapping Community[J]. Information Studies: Theory & Application, 2021, 44(6):164-170.)
[28] 张晓丹. 改进的图神经网络文本分类模型应用研究——以NSTL科技期刊文献分类为例[J]. 情报杂志, 2021, 40(1):184-188.
[28] (Zhang Xiaodan. The Application of Improved Graph Neural Network in Big Data Classification of Scientific and Technological Documents[J]. Journal of Intelligence, 2021, 40(1):184-188.)
[29] 秦成磊, 章成志. 基于层次注意力网络模型的学术文本结构功能识别[J]. 数据分析与知识发现, 2020, 4(11):26-42.
[29] (Qin Chenglei, Zhang Chengzhi. Recognizing Structure Functions of Academic Articles with Hierarchical Attention Network[J]. Data Analysis and Knowledge Discovery, 2020, 4(11):26-42.)
[30] Wu Z, Pi D, Chen J, et al. Rumor Detection Based on Propagation Graph Neural Network with Attention Mechanism[J]. Expert Systems with Applications, 2020, 158:113595.
doi: 10.1016/j.eswa.2020.113595
[31] 范涛, 王昊, 吴鹏. 基于图卷积神经网络和依存句法分析的网民负面情感分析研究[J]. 数据分析与知识发现, 2021, 5(9):97-103.
[31] (Fan Tao, Wu Hao, Wu Peng. Sentiment Analysis of Online Users’ Negative Emotions Based on Graph Convolutional Network and Dependency Parsing[J]. Data Analysis and Knowledge Discovery, 2021, 5(9):97-103.)
[32] 顾耀文, 张博文, 郑思, 等. 基于图注意力网络的药物ADMET分类预测模型构建方法[J]. 数据分析与知识发现, 2021, 5(8):76-85.
[32] (Gu Yaowen, Zhang Bowen, Zheng Si, et al. Building a Drug ADMET Classification Prediction Model Based on Graph Attention Network[J]. Data Analysis and Knowledge Discovery, 2021, 5(8):76-85.)
[33] Freeman L C. Centrality in Social Networks Conceptual Clarification[J]. Social Networks, 1978, 1(3):215-239.
doi: 10.1016/0378-8733(78)90021-7
[34] 陈思菁, 李纲, 毛进, 等. 突发事件信息传播网络中的关键节点动态识别研究[J]. 情报学报, 2019, 38(2):178-190.
[34] (Chen Sijing, Li Gang, Mao Jin, et al. Dynamic Identification of Key Nodes in Information Propagation Networks During Emergencies[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(2):178-190.)
[35] Liu W Y, Wen Y D, Yu Z D, et al. Large-Margin Softmax Loss for Convolutional Neural Networks[C]// Proceedings of the 33rd International Conference on Machine Learning. 2016.
[36] Aur D, Vila-Rodriguez F. Dynamic Cross-Entropy[J]. Journal of Neuroscience Methods, 2017, 275:10-18.
doi: 10.1016/j.jneumeth.2016.10.015
[37] Ng A Y. Feature Selection, L1 vs. L2 Regularization, and Rotational Invariance[C]// Proceedings of the 21st International Conference on Machine Learning. 2004: 78-85.
[38] Chujo K, Utiyama M. Understanding the Role of Text Length, Sample Size and Vocabulary Size in Determining Text Coverage[J]. Reading in a Foreign Language, 2005, 17(1):1-22.
[39] Leebron E J. Visual Persuasion: The Role of Images in Advertising[J]. Journal of Broadcasting and Electronic Media, 1997, 41(4):589-592.
[40] Kakol M, Nielek R, Wierzbicki A. Understanding and Predicting Web Content Credibility Using the Content Credibility Corpus[J]. Information Processing and Management, 2017, 53(5):1043-1061.
doi: 10.1016/j.ipm.2017.04.003
[41] Rokova V, George E I. EMVS: The EM Approach to Bayesian Variable Selection[J]. Journal of the American Statistical Association, 2014, 109(506):828-846.
doi: 10.1080/01621459.2013.869223
[42] Blei D M, Ng A Y, Jordan M I. Latent Dirichlet Allocation[J]. Journal of Machine Learning Research, 2003, 3(4-5):993-1022.
[43] Kingma D, Ba J. A Method for Stochastic Optimization[OL]. arXiv Preprint, arXiv: 1412.6980v9.
[44] Gonzalez R C. Deep Convolutional Neural Networks[J]. IEEE Signal Processing Magazine, 2018, 35(6):79-87.
doi: 10.1109/MSP.2018.2842646
[45] Zhou S Q, Ding L X, Zhang J, et al. Linearization Learning Method of BP Neural Networks[J]. Wuhan University Journal of Natural Sciences, 1997, 2(1):35-39.
doi: 10.1007/BF02834910
[46] Nick G, Matthias S. Support Vector Machines[J]. Stata Journal, 2016, 16(4):917-937.
doi: 10.1177/1536867X1601600407
[47] Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32.
doi: 10.1023/A:1010933404324
[1] Shan Xiaohong,Wang Chunwen,Liu Xiaoyan,Han Shengxi,Yang Juan. Identifying Lead Users in Open Innovation Community from Knowledge-based Perspectives[J]. 数据分析与知识发现, 2021, 5(9): 85-96.
[2] Gu Yaowen, Zhang Bowen, Zheng Si, Yang Fengchun, Li Jiao. Predicting Drug ADMET Properties Based on Graph Attention Network[J]. 数据分析与知识发现, 2021, 5(8): 76-85.
[3] Li He,Zhu Linlin,Yan Min,Liu Jincheng,Hong Chuang. Identifying Useful Information from Open Innovation Community[J]. 数据分析与知识发现, 2018, 2(12): 12-22.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938