Social network analysis based on canteen transaction records

Xiangyu Chang (Center of Data Science and Information Quality, and Department of Information Management and E-business, School of Management, Xi’an Jiaotong University, Xi’an Shaanxi, China)

Dehai Di (Department of Information Management and E-business, School of Management, Xi’an Jiaotong University, Xi’an, Shaanxi, China)

Wei Huang (Department of Information Management and E-business, School of Management, Xi’an Jiaotong University, Xi’an, Shaanxi, China)

Abstract

College students’ social network could influence their academic performance, attrition, and even mental health. Unfortunately, it is not easy to collect college students’ social network information. The existing methods (e.g., collecting data via survey, online social network web sites and phone call records) suffer from various shortcomings. In this paper we present a case study in which the college students’ social network is extracted from their canteen transaction records. In detail, we empirically collect a canteen transaction data set which has 4.5 million transaction records of 16 thousand undergraduate students during one semester (112 days). We propose a systematic method to extract the canteen social network. Based on the extracted network, we calculate some network attributes for each student, and employ regression analysis to study the relationships between students’ network attributes and academic performance. The findings of this case study encourage us to build more rigorous statistical methods to extract social network from transaction records, and to examine the effects of network attributes.

Keywords

social network analysis, canteen transaction records, academic performance

2010 Mathematics Subject Classification

91D30

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Y. Liu was partially supported by the National Natural Science Foundation of China (Project No. 71301128, 71331005, 91546119) and the China Postdoctoral Science Foundation (Project No. 2014M560795, 2015T81039). X. Chang was partially supported by the National Natural Science Foundation of China (Project No. 11401462, 61502342, 61603162) and the China Postdoctoral Science Foundation (Project No. 2015M582630).

Received 23 July 2016

Published 23 August 2017