Analysis and prediction of GitHub company influence based on machine learning

Mingyu WANG; Qingyuan GONG; Jingjing QU; Xin WANG

doi:10.11959/j.issn.2096-6652.202327

您当前的位置：

首页 >

文章列表页 >

Analysis and prediction of GitHub company influence based on machine learning

Special Column: Intelligent Technology and Social Computing | 更新时间：2024-04-04

- Analysis and prediction of GitHub company influence based on machine learning
- Chinese Journal of Intelligent Science and Technology Vol. 5, Issue 3, Pages: 330-342(2023)
- 作者机构：
  
  1. 复旦大学计算机科学技术学院，上海 200438
  2. 复旦大学智能复杂体系基础理论与关键技术实验室，上海 200438
  3. 上海人工智能实验室，上海 201210
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-6652.202327
  CLC： TP39
- Revised：2023-08-02，
  
  Online First：2023-09，
  
  Published：15 September 2023
- 稿件说明：
移动端阅览
Mingyu WANG, Qingyuan GONG, Jingjing QU, et al. Analysis and prediction of GitHub company influence based on machine learning[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 330-342.
DOI：

Mingyu WANG, Qingyuan GONG, Jingjing QU, et al. Analysis and prediction of GitHub company influence based on machine learning[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(3): 330-342. DOI： 10.11959/j.issn.2096-6652.202327.

摘要

企业影响力的高低不仅关系到其行业竞争力，也影响着其社会声誉和未来发展，然而对企业影响力的评价一直没有统一的标准。GitHub是一个代表性的软件开发代码存储库开源平台，现有研究通常使用企业在GitHub发布的项目得到的star总数衡量其影响力高低，但是这种方式难以衡量小微企业和新生企业的潜力。通过引入科学家的影响力衡量指标h指数，以GitHub为信息源进行企业网络建模，同时基于该网络提取特征构建分类器，对企业未来的影响力水平进行预测。在此基础上应用SHAP模型解释技术，判别决定企业影响力的重要特征。实验结果显示，基于XGBoost的模型在GitHub真实数据集上实现了0.92的准确率和0.93的平均AUC，可以准确、可靠地对企业进行影响力预测。

Abstract

The influence of a company is not only related to its industry competitiveness

but also affects its public reputation and future development.However

there has been no unified standard for evaluating the influence of a company.GitHub is a representative open-source platform for software development code repositories.Existing research typically used the total number of stars a company receives for projects posted on GitHub to measure its influence

but this approach is difficult to measure the potential of small

micro

and nascent companies.The paper predicted the future influence level of a company by introducing the scientist's influence measure h-index

using GitHub as the information source

and modeling the company network.Features was extracted features based on this network to build the classifier

which predicted the future influence level of the company.The SHAP model explanation technique was further applied on this basis to identify the important features that determined the influence of a company.The experimental results showed that the XGBoost model achieved an accuracy of 0.92 and an average AUC of 0.93 on the real-world GitHub dataset.In summary

the proposed method could accurately and reliably predict the influence of companies.

关键词

Keywords

references

GONG Q Y , LIU Y S , ZHANG J Y , et al . Detecting malicious accounts in online developer communities using deep learning [C ] // Proceedings of IEEE Transactions on Knowledge and Data Engineering . Piscataway:IEEE Press , 2023 : 1 - 17 .

LIU C , YANG D , ZHANG X H , et al . Recommending GitHub projects for developer onboarding [J ] . IEEE Access , 2018 , 6 : 52082 - 52094 .

李占山 , 刘兆赓 . 基于XGBoost的特征选择算法 [J ] . 通信学报 , 2019 , 40 ( 10 ): 101 - 108 .

LI Z S , LIU Z G . Feature selection algorithm based on XGBoost [J ] . Journal on Communications , 2019 , 40 ( 10 ): 101 - 108 .

李变 . 基于GitHub社交网络中用户影响力评估算法的研究 [D ] . 西安:西安电子科技大学 , 2015 .

LI B . Research on user influence evaluation algorithm in GitHub-based social networks [D ] . Xi’an:Xidian University , 2015 .

HU Y , WANG S , REN Y , et al . User influence analysis for GitHub developer social networks [J ] . Expert Systems with Applications , 2018 , 108 : 108 - 118 .

BADASHIAN A S , STROULIA E . Measuring user influence in GitHub:the million follower fallacy [C ] // Proceedings of the 3rd International Workshop on Crowd Sourcing in Software Engineering . New York:ACM , 2016 : 15 - 21 .

LI S Y , YANG J , LUENG G , et al . SybilFlyover:heterogeneous graphbased fake account detection model on social networks [J ] . KnowledgeBased Systems , 2022 ,258:110038.

ZHANG P C , XIONG F , LEUNG H , et al . FunkR-pDAE:personalized project recommendation using deep learning [J ] . IEEE Transactions on Emerging Topics in Computing , 2021 , 9 ( 2 ): 886 - 900 .

BORGES H , HORA A , VALENTE M T . Predicting the popularity of GitHub repositories [C ] // Proceedings of the 12th International Conference on Predictive Models and Data Analytics in Software Engineering . New York:ACM , 2016 : 1 - 10 .

VARUNA T V , MOHAN A . Trend prediction of GitHub using time series analysis [C ] // Proceedings of 2019 10th International Conference on Computing,Communication and Networking Technologies (ICCCNT) . Piscataway:IEEE Press , 2019 : 1 - 7 .

HAN J X , DENG S G , XIA X , et al . Characterization and prediction of popular projects on GitHub [C ] // Proceedings of 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC) . Piscataway:IEEE Press , 2019 : 21 - 26 .

REN L M , SHAN S M , XU X J , et al . StarIn:an approach to predict the popularity of GitHub repository [M ] // Communications in Computer and Information Science . Singapore : Springer Singapore , 2020 : 258 - 273 .

RAHMAN M M , ROY C K , COLLINS J A . CORRECT:code reviewer recommendation in GitHub based on cross-project and technology experience [C ] // Proceedings of 2016 IEEE/ACM 38th International Conference on Software Engineering Companion (ICSE-C) . Piscataway:IEEE Press , 2017 : 222 - 231 .

LUNDBERG S M , ERION G , CHEN H , et al . From local explanations to global understanding with explainable AI for trees [J ] . Nature Machine Intelligence , 2020 , 2 ( 1 ): 56 - 67 .

贾俊 , 张斌 , 李志远 , 等 . 基于用户行为分析的个性化推荐算法 [J ] . 智能科学与技术学报 , 2019 , 1 ( 4 ): 421 - 426 .

JIA J , ZHANG B , LI Z Y , et al . Personalized recommendation algorithm based on user behavior analysis [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 4 ): 421 - 426 .

THUNG F , BISSYANDÉ T F , LO D , et al . Network structure of social coding in GitHub [C ] // Proceedings of 2013 17th European Conference on Software Maintenance and Reengineering . Piscataway:IEEE Press , 2013 : 323 - 326 .

陈妍 , 罗雪琴 , 梁伟 , 等 . 基于情感信息融合注意力机制的抑郁症识别 [J ] . 智能科学与技术学报 , 2022 , 4 ( 4 ): 600 - 609 .

CHEN Y , LUO X Q , LUENG W , et al . Depression recognition based on emotional information fused with attentional mechanism [J ] . Chinese Journal of Intelligent Science and Technology , 2022 , 4 ( 4 ): 600 - 609 .

陈德旺 , 蔡际杰 , 黄允浒 . 面向可解释性人工智能与大数据的模糊系统发展展望 [J ] . 智能科学与技术学报 , 2019 , 1 ( 4 ): 327 - 334 .

CHEN D W , CAI J J , HUANG Y H . Development prospect of fuzzy system oriented to interpretable artificial intelligence and big data [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 4 ): 327 - 334 .

ZHAO G , DA C , ZOU Y , et al . Improving the pull requests review process using learning-to-rank algorithms [J ] . Empirical Software Engineering , 2019 , 24 ( 4 ): 2140 - 2170 .

RIQUELME F , VERA J A . A parameterizable influence spread-based centrality measure for influential users detection in social networks [J ] . Knowledge-Based Systems , 2022 ,257:109922.

PANCHENDRARAJAN R , SAXENA A . Topic-based influential user detection:a survey [J ] . Applied Intelligence , 2023 , 53 ( 5 ): 5998 - 6024 .

张钹 . 人工智能进入后深度学习时代 [J ] . 智能科学与技术学报 , 2019 , 1 ( 1 ): 4 - 6 .

ZHANG B . Artificial intelligence is entering the post deep-learning era [J ] . Chinese Journal of Intelligent Science and Technology , 2019 , 1 ( 1 ): 4 - 6 .

Views

770

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Research on popularity stage prediction methods for open-source community projects: a case study of large language model projects

Intelligent regulation for cryptocurrency anti-money laundering: models, methods, and applications

Sidelink resource allocation algorithm of C-V2X based on deep Q learning

Theoretical framework of brain modelling and highlighted problems

A survey on evolutionary ensemble learning algorithm

Related Author

LI Cong

FU Xiaoming

CHEN Yang

LU Xiaoyi

ZHANG Yifei

WANG Fei-Yue

YANG Dong

YUAN Yong

Related Institution

College of Computer Science and Artificial Intelligence, Fudan University

Adaptive Networks and Control Laboratory, College of Future Information Technology, Fudan University

Research Center for Social Intelligence, Fudan University

Institute of Computer Science, University of Göttingen, Göttingen

School of Mathematics, Renmin University of China

Postal code：100079
Tel：（010）53879206 Email：cjist@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09064830号-19 京公网安备11010802024621
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.
Total Visits：162003 Visits Today：166

⁰