Inferring and analysis of social networks using RFID check-in data in China

Abstract

Social networks play an important role in our daily lives. However, social ties are rather elusive to quantify, especially for large groups of subjects over prolonged periods of time. In this work, we first propose a methodology for extracting social ties from long spatio-temporal data streams, where the subjects are 17,795 undergraduates from a university of China and the data streams are the 9,147,106 time-stamped RFID check-in records left behind by them during one academic year. By several metrics mentioned below, we then analyze the structure of the social network. Our results center around three main observations. First, we characterize the global structure of the network, and we confirm the small-world phenomenon on a global scale. Second, we find that the network shows clear community structure. And we observe that younger students at lower levels tend to form large communities, while students at higher levels mostly form smaller communities. Third, we characterize the assortativity patterns by studying the basic demographic and network properties of users. We observe clear degree assortativity on a global scale. Furthermore, we find a strong effect of grade and school on tie formation preference, but we do not find any strong region homophily. Our research may help us to elucidate the structural characteristics and the preference of the formation of social ties in college students’ social network.

Finding College Student Social Networks by Mining the Records of Student ID Transactions

“…In the fixed time slicing method, time is sliced into consecutive, non-overlapping time slots ∆t with equal length [17]. In the above equation, w…”

“…In this paper, we used the subset of the data from Reference [17], which include the check-in data at the food courts and the supermarkets of 662 undergraduate students of the College of Physical Science and Technology (CPST), CCNU. These data are recorded by the student ID card system.…”

“…College students' social networks have significant homophily-intra-group students have larger co-occurrence counts, while inter-group students have smaller co-occurrence counts even if they have social ties. In References [16,17], it is assumed that all students are mutually independent and the homophily effect is ignored. Under this assumption, inter-group social ties are buried.…”

“…Yao et al [16] used the consecutive check-in records of each student pair to infer the students' social network. Liu et al [17] used the students' dining transaction data and employed a fixed time slicing method. In the latter, they sliced the time into 5-min slots and if two students appeared in the same slot, then one co-occurrence was counted.…”

“…Firstly, although the fixed time slicing method from Reference [17] shows high computational efficiency, transaction data may be sliced into different time slots so that some co-occurrences may get lost.…”