基于机器学习的GitHub企业影响力分析与预测

doi:10.11959/j.issn.2096-6652.202327

Abstract

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.

Key words: online developer community, social network, machine learning, SHAP

CLC Number:

TP39

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.

Figures/Tables 11

References 22

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企业名称	员工数量/名	创立时间	star总数/次	h指数
Tencent	110 715	1998年	448 913	135
Square	1 853	2009年	281 166	105
IBM	282 100	1911年	66 368	103
Apple	154 000	1976年	217 340	101
Facebook	83 553	2004年	935 897	97
JetBrains	1 900	2000年	161 409	97
Uber	29 000	2009年	98 124	82
Twitter	7 500	2006年	123 067	69
Oracle	143 000	1977年	56 345	64

排名	度量中心性	介数中心性	接近中心性	特征向量中心性
1	'microsoft': 0.522	'microsoft': 0.110	'microsoft': 0.554	'microsoft': 0.164
2	'shopify': 0.470	'shopify': 0.070	'shopify': 0.521	'shopify': 0.162
3	'unity-technologies': 0.406	'unity-technologies': 0.030	'unity-technologies': 0.486	'unity-technologies': 0.158
4	'atlassian': 0.359	'automattic': 0.026	'atlassian': 0.461	'atlassian': 0.151
5	'nokia': 0.346	'atlassian': 0.021	'nokia': 0.456	'nokia': 0.149
6	'github': 0.338	'github': 0.019	'github': 0.454	'github': 0.1 464
7	'apache': 0.326	'apache': 0.019	'apache': 0.446	'apache': 0.144
8	'automattic': 0.282	'nokia': 0.017	'automattic': 0.431	'grafana': 0.139
9	'grafana': 0.280	'mozilla': 0.014	'grafana': 0.429	'segmentio': 0.135
10	'mozilla': 0.274	'JetBrains': 0.009	'mozilla': 0.428	'hashicorp': 0.134
11	'elastic': 0.266	'facebook': 0.009	'elastic': 0.423	'elastic': 0.133
12	'segmentio': 0.264	'intel': 0.008	'segmentio': 0.422	'hubspot': 0.132
13	'facebook': 0.260	'adobe': 0.006	'facebook': 0.421	'appian': 0.131
14	'hashicorp': 0.260	'alibaba': 0.006	'hashicorp': 0.421	'nextdoor': 0.131
15	'hubspot': 0.256	'meteor': 0.006	'hubspot': 0.419	'digitalocean':0.130

预测模型	参数	accuracy	recall	F1-score	precision
XGBoost	learning_rate=0.5, n_estimators=80, min_child_weight=5, subsample=0.8	0.918	0.914	0.889	0.865
SVM	kernel=`linear`, C=16	0.800	0.886	0.861	0.838
MLP	solver=`lbfgs`, hidden_layer_sizes=(5,2), max_iter=1000	0.837	0.714	0.756	0.806

模型	accuracy	recall	F1-score	precision
本文预测模型	0.918	0.914	0.889	0.865
Han等人提出的模型^[11]	0.865	0.816	0.827	0.838

Analysis and prediction of GitHub company influence based on machine learning

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