摘要
从海量新闻标题、微博等短文本异源数据及早、准确地发现舆情话题及其演化规律,以期为政府和企业监管舆情态势提供参考。以大规模异源在线社会网络数据为研究源,以文本关键词共现为建模依据点构建时变话题网络模型,将异源在线网络话题早发现及演化追踪问题转变为时变话题网络的动态社团发现与社团演化规律研究问题。进一步,提出以Louvain算法为迭代核心且以模块度增益和网络局部增量为算法量化对象的时变网络动态Louvain社团发现方法,通过与静态Louvain社团发现算法在大量计算机仿真和真实异源社会媒体数据下实验对比分析,证明所建话题网络和动态Louvain社团算法可在较少时空代价下快速、有效地实现对异源在线社会网络隐匿话题的早发现和演化规律追踪。
The purpose of this study is to accurately identify the new emerging topic immediately and to identify its evolution law from all kinds of heterogeneous on-line social network data which are composed of short texts like news titles and micro-blogs,the results of which will provide valuable decision support for government officers and company administrators.Firstly,all kinds of heterogeneous on-line social network data are acquired and modeled as the time-varying network by using the co-occurrence of short texts’keywords,from which the topic early detection problem could be changed into the problem of dynamic community detection on the time-varying topic network;Secondly,we propose the dynamic community detection method with static Louvain algorithm embedded and with modularity gain and local network variation as quantification values.The proposed method yields preferable results under both large amount of computer-generated data and real heterogeneous online social network data,community detection and propagation identification results under computer-generated and real heterogeneous online social network data validates the algorithms’efficiency and feasibility.
作者
徐小艳
吕伟
张贝贝
周帅鹏
魏嵬
XU Xiaoyan;LV Wei;ZHANG Beibei;ZHOU Shuaipeng;WEI Wei(School of Science,Xi'an Shiyou University,Xi'an 710065;School of Computer and Engineering,Xi'an University of Technology,Xi'an 710048;Company of Aamaze Data,Xi'an 710061)
出处
《工程数学学报》
CSCD
北大核心
2023年第3期341-354,共14页
Chinese Journal of Engineering Mathematics
基金
国家重点研发计划(2018YFB0203900)。
