%0 Journal Article
%T COT:一种连续时间序列建模的社区发现算法<br>COT: acontinuous temporal modeling algorithm for community discovery
%A 吴平杰
%A 周斌
%A 吴泉源&lt
%A br&gt
%A WU Ping-jie
%A ZHOU Bin
%A WU Quan-yuan
%J 山东大学学报（理学版）
%D 2016
%R 10.6040/j.issn.1671-9352.2.2015.305
%X 摘要： 研究基于交互及内容数据发现交往密切的交互社区,以及这些社区如何随时间发展变化,对于网络营销、内容推荐等应用具有重要意义。已有的基于内容与链接分析的混合模型大都未能对交互行为中广泛存在、且显著影响社区结构的时序信息进行统一建模分析。基于贝叶斯图模型,提出了一种可综合考虑交互信息、网络结构以及交互行为时间信息的社区发现模型COT(community over time),可用于从在线社交网络的交互数据中发现具有特定主题倾向及周期性行为模式的动态交互社区。模型采用Gibbs采样进行贝叶斯统计推断,通过在新浪微博真实数据集上的实验验证,可以有效应用于在线社交网络中并取得较高的精细度和可解释性。<br>Abstract: It is very important to discover closely-connected interactive communities, as well as mining their evolution patterns, by analyzing the content information, to benefit network marketing, content recommendation and other online applications. To the best of our knowledge, most of the hybrid models based on the content and link analysis failed to integrate the timestamp information into a unified model, and thus failed to use the temporal information as well. In this paper, we propose a new community discovery model, COT(community over time), based on the Bayesian graphical model, which can integrate textual content, topology structure and temporal information. Experimental evaluation on a real dataset from the SinaWeibo was performed, and the result shows that our model has obvious effect for online social networks and obtains better interpretability
%U http://lxbwk.njournal.sdu.edu.cn/CN/10.6040/j.issn.1671-9352.2.2015.305