校园生活中友谊网络的演化特征与行为特征
Evolution Features and Behavior Characters of Friendship Networks on Campus Life
摘要：
从大数据中分析和挖掘学生的行为和互动，是教育数据采掘必不可少的一部分。基于校园灵巧卡数据，包括3万多名匿名学生的静态人口统计信息和动态行为数据，本文研究了友谊的演化特征以及行为特征与学生交往的关系。一方面，利用本文提出的从月度消费记录中提取的朋友关系，构建了四种不同的演化友谊网络。此外，利用社会网络分析（SNA）和逾渗理论分析了友谊网络的巨连通分量（GCC）特征。另一方面，本研究选取了两个高层次的行为特征，即有序性和勤奋性，来分析它们与学生互动之间的关系。我们的实验/实证结果表明，友谊网络的规模随着时间的增长而下降，小世界效应和幂律度分布在友谊网络中被发现。其次，有序性和勤奋性的相关系数的结果证实了学生之间存在较强的同伴效应。最后，对友谊网络的有序性进行了渗流分析，结果表明友谊网络中存在一个相变，这对通过干预相变点附近的关键学生来实现群体智能具有启发意义。
英文摘要：
Analyzing and mining students' behaviors and interactions from big data is an essential part of education data mining. Based on the data of campus smart cards, which include not only static demographic information but also dynamic behavioral data from more than 30000 anonymous students, in this paper, the evolution features of friendship and the relations between behavior characters and student interactions are investigated. On the one hand, four different evolving friendship networks are constructed by means of the friend ties proposed in this paper, which are extracted from monthly consumption records. In addition, the features of the giant connected components (GCCs) of friendship networks are analyzed via social network analysis (SNA) and percolation theory. On the other hand, two high-level behavior characters, orderliness and diligence, are adopted to analyze their associations with student interactions. Our experiment/empirical results indicate that the sizes of friendship networks have declined with time growth and both the small-world effect and power-law degree distribution are found in friendship networks. Second, the results of the assortativity coefficient of both orderliness and diligence verify that there are strong peer effects among students. Finally, the percolation analysis of orderliness on friendship networks shows that a phase transition exists, which is enlightening in that swarm intelligence can be realized by intervening the key students near the transition point.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
