华东师范大学学报（自然科学版）Issue(5)：87-98,12.DOI:10.3969/j.issn.1000-5641.2025.05.009
基于学生开源社区行为的数字岗位就业预测
Student employment prediction for digital jobs based on behavior in open-source communities
摘要
Abstract
Accurately predicting students'post-graduation career paths plays a vital role in talent development in higher education and in refining recruitment strategies in industry.Most existing employment prediction research relies heavily on academic or campus-related data,while overlooking the role of students'open-source contributions in the process of securing digital-related positions.This study addresses employment prediction for digital roles by analyzing students'behaviors in open-source communities.We construct a heterogeneous graph comprising student nodes,code repository nodes,and various semantic relationships to model students'expertise.To enhance prediction performance,we propose two strategies that integrate large language model(LLM)with graph neural networks:LLM-as-Encoder and LLM-as-Explainer.Experiments on our curated dataset show that the proposed approach outperforms baseline methods,achieving improvements of 7.71%in accuracy and 9.19%in Macro-F1.By leveraging open-source activity,this study supports data-driven decision-making for university career services,aids enterprises in identifying technical talent,and provides students with actionable insights for career planning.关键词
开源社区行为/学生就业预测/异构信息网络/图神经网络/大语言模型Key words
open-source community behavior/student employment prediction/heterogeneous information network/graph neural network/large language model分类
信息技术与安全科学引用本文复制引用
谢林娜,陆雪松..基于学生开源社区行为的数字岗位就业预测[J].华东师范大学学报（自然科学版）,2025,(5):87-98,12.基金项目
国家自然科学基金(62277017) （62277017）
