文献类型：期刊文献

中文题名：基于最短时间距离的校园无线网络用户关联性度量

英文题名：Correlation measurement of campus wireless network users based on the shortest time distance

作者：李鑫健[1];刘漫丹[1]

机构：[1]华东理工大学信息科学与工程学院

年份：2019

卷号：41

期号：10

起止页码：1755

中文期刊名：计算机工程与科学

外文期刊名：Computer Engineering & Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

语种：中文

中文关键词：用户轨迹数据;时空序列模型;用户关联性度量;聚类分析

外文关键词：trajectory data;time series model;measurement of users’correlation;cluster analysis

摘要：在校园网络中,存在着大量的信息系统,记录着用户的日常行为信息。通过对大量用户的日常轨迹信息分析,可以发现用户之间的行为关联性,度量用户之间的社会关系强度。基于上海某校的校园网络数据特点,提出了一种改进的基于用户时间序列模型,用最短时间距离进行社会关系度量的方法。该方法首先依据用户的行为数据生成用户行为时间序列,并在此基础上进行行为关联性的度量,以反映用户在真实世界中的社会关系强度,并利用地点访问热度修正社会关系强度的分析结果。实验中使用该方法对上海某校的校园网数据进行分析,度量用户关联性强度,验证了该方法的有效性。
In campus networks,there are a large number of information systems that record the users’daily behavior.By analyzing the daily trajectory information of a large number of users,we can find the behavioral correlation among users,and measure the strength of social relationship among users.Based on the data characteristics of the campus network systemin a Shanghai college,we propose an improved method based on user time series model,which utilizes the shortest time distance to measure the social relationship among users.This method firstly uses the users’behavioral data to generate the time series for users.Based on the time series,it measures the behavioral correlation between two users to quantify the strength of users’social relationship in the real world.Location popularity is used to correct the analysis of the social relationship strength.In the experiment,we apply the method to analyze the data of the campus network system in one Shanghai college,measue the strength of correlation among users,and verify the effectiveness of the method.