计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 680-685.doi: 10.11896/jsjkx.210800123

• 交叉&应用 • 上一篇    下一篇

数据科学与大数据技术课程体系的复杂网络分析

杨波, 李远彪   

  1. 昆明理工大学数据科学研究中心 昆明 650500
    昆明理工大学理学院 昆明 650500
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 杨波(yangbo@kust.edu.cn)
  • 基金资助:
    国家自然科学基金(11947041)

Complex Network Analysis on Curriculum System of Data Science and Big Data Technology

YANG Bo, LI Yuan-biao   

  1. Data Science Research Center,Kunming University of Science and Technology,Kunming 650500,China
    Faculty of Science,Kunming University of Science and Technology,Kunming 650500,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:YANG Bo,born in 1987,Ph.D.His main research interests include statistical physics and complex systems,complex network,text data mining and visualization.
  • Supported by:
    National Natural Science Foundation of China(11947041).

摘要: 近年来,越来越多的高校开始开设数据科学与大数据技术专业,作为一个多学科交叉的新兴热门宽口径专业,其课程体系仍在进一步完善中。文中运用复杂网络方法对从互联网上收集到的106所高校的课程数据进行了分析和可视化,分别构建了课程共现网络和开设院校关系网络。对于耦合度较高的课程共现网络,提出了一种基于边权的壳层分解算法,对课程重要性进行逐层分析,并将所得结果与词频统计和由Apriori算法获取的频繁项集结果进行了对比分析。考虑到该专业可授予理学或工学学位,又将数据集划分为理学和工学两部分进行了分析和可视化。本研究的开展能够给即将开设或者已经开设数据科学与大数据技术专业的院校提供一定的参考,同时也为高耦合网络的分析提供一种有效的算法。

关键词: 复杂网络, 壳层分解算法, 课程共现网络, 频繁项集

Abstract: In recent years,more and more universities have begun to offer majors in data science and big data technology.As an emerging and popular multi-disciplinary major with wide caliber,its curriculum system is still being furthered improved.In this paper,we use complex network methods to analyze and visualize the course data set of 106 universities collected from the Internet.The course co-occurrence network and college relationship network are constructed respectively.For the highly coupled course co-occurrence network,a shell decomposition algorithm based on edge weights is proposed.The results are compared with the word frequency statistics and the frequent items obtained by the Apriori algorithm.Considering that this speciality can award a degree in science or engineering,the data set is divided into two sections science and engineering to analyze and visualize.This research can provide a certain reference to universities which are establishing or have established data science and big data technology speciality,and also provide an effective algorithm for the analysis of highly coupled networks.

Key words: Complex network, Course co-occurrence network, Frequent items, Shell decomposition algorithm

中图分类号: 

  • TP391
[1] MEI H.Introduction to big data[M].Beijing:Higher Education Press,2018.
[2] CHAO L M,XING C X,WANG Y Q.Unique curriculums for data science and big data technology[J].Computer Science,2018,45(3):3-10.
[3] LI S S,ZHOU J W,TANG J T,et al.Curriculum analysis of data science and big data specialty[J].Computer Engineering,2018,40(S1):109-113.
[4] WANG X F,LI X,CHEN G R.Network Science: An Introduction[M].Beijing:Higher Education Press,2012.
[5] YANG B,LI J H.Complex network analysis of three-way decision researches[J].International Journal of Machine Learning and Cybernetics,2020,11:973-987.
[6] LI S.Mapping ancient remedies:applying a network approach to traditional chinese medicine[J].Science,2015,350(6262):S72-S74.
[7] LIU X F,TSE C K,SMALL M.Complex network structure of musical compositions:Algorithmic generation of appealing mu-sic[J].Physica A Statistical Mechanics and Its Applications,2010,389(1):126-132.
[8] STILLER J,HUDSON M.Weak Links and Scene Cliques Withinthe Small World of Shakespeare[J].Journal of Evolutionary Psychology,2005,3(1):57-73.
[9] AGRAWAL R,SRIKANT R.Fast algorithms for mining asso-ciation rules[C]//Proceedings of the 20th International Confe-rence on Very Large Data Bases.1994:487-499.
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