社交网络中隐式事件突发性检测  

Latent Event-related Burst Detection in Social Networks

作　　者：介飞 谢飞[2] 李磊[1] 吴信东[1,3] JIE Fei;XIE Fei;LI Lei;WU Xin-Dong(School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China;Department of Computer Science and Technology, Hefei Normal University, Hefei 230601, China;School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette LA 70503, USA)

机构地区：[1]合肥工业大学计算机与信息学院 [2]合肥师范学院计算机科学与技术系 [3]路易斯安那大学拉菲特分校计算与信息学院

出　　处：《自动化学报》2018年第4期730-742,共13页Acta Automatica Sinica

基　　金：国家重点基础研究发展计划(973计划)(2013CB329604);国家自然科学基金(61503114;61503116)资助~~

摘　　要：社交网络与人们的生活息息相关,其上的用户行为可用于检测社交网络中的事件突发性,进而准确定位事件的发生区间.但用户行为易受主观及外部因素的影响,有时会出现隐式事件突发性,给事件突发性检测带来困难.本文针对社交网络中的隐式事件突发性问题,在以社交行为特征进行事件突发性检测的基础上,引入关键词特征,动态调整各个时间窗口的候选关键词,将不同事件与不同的关键词特征绑定,避免事件之间及噪音带来的干扰,实现对隐式事件突发性的准确识别.相关实验表明,本文提出的算法可有效改善现有社交网络中事件突发性检测任务的效果.Social networks are closely bound up with our daily life, in which behaviors of users can be used for detection of event-related bursts and further for determination of the time period for each event. But latent event-related bursts,which result from internal or external impacts on users＇ behaviors, will be difficult to identify. In this paper, in order to solve the detection problem of latent event-related bursts in social networks, on the basis of event burst detection via social behavior features, we introduce the features of keywords and dynamically change the keyword candidates for each time window, so as to bind different events with different keywords, aiming to avoid interferences from inter-events or noise and discover latent event-related bursts more accurately. Experimental results show that our proposed method can improve the performance of event-related burst detection in social networks compared with existing algorithms.

关 键 词：突发性 事件 检测 社交网络 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]