结合用户兴趣的微博信息传播模式挖掘  

User Interest Related Information Diffusion Pattern Mining in Microblog

作　　者：郝志峰[1,2] 黄灿锦 蔡瑞初[1] 温雯[1] 黄宇鹏[1] 陈炳丰[1] 

机构地区：[1]广东工业大学计算机学院,广州510006 [2]佛山科学技术学院数学与大数据学院,佛山5280000

出　　处：《模式识别与人工智能》2016年第10期924-935,共12页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金项目(No.61572143;61472089;61202269);广东省自然科学基金项目(No.2014A030306004;2014A030308008);广东省科技计划项目(No.2015B010108006;2013B051000076;2012B01010029)资助~~

摘　　要：由于信息传播模型是社区挖掘、社区影响力研究的基础,文中提出结合用户兴趣的信息传播模型,设计基于频繁子树的信息传播微观模式挖掘方法.首先,基于微博社交网络图表示及用户多标签建模,将微观信息传播模式转换为频繁子树挖掘问题.然后,针对微博社交网络图单节点多标签特性,设计多标签节点树的频繁子树挖掘算法(MLTree Miner).最后,结合主题提取方法,使用MLTree Miner挖掘信息传播模式.在人工数据集上的实验表明,MLtree Miner能高效地对多标签节点树进行频繁子树挖掘.针对新浪微博真实数据的实验也验证方法的有效性.Information diffusion modeling is the basis of the community mining and community influence research. Based on a user interest related information diffusion model, a microscopic pattern mining method is proposed to detect the information diffusion features using frequent subtree mining in this paper. Firstly, microscopic information diffusion pattern is converted into frequent subtrees mining by formulating social network in microblog as a series of graphs with users multiple labels. In terms of the microblog social network characteristics of multiple labels on single node, an efficient frequent subtrees mining algorithm on the tree with multiple labels tree miner （MLTreeMiner） is proposed. Finally, combined with topic information extraction method, MLTreeMiner is used to mine information diffusion patterns. Experiments on synthetic data demonstrate that MLTreeMiner is efficient for frequent subtrees mining on the tree with multiple labels. Experiments are also carried out on real data from Sina Weibo, and the validity of the MLTreeMinner is verified.

关 键 词：社交网络 用户兴趣 传播模式 频繁子树挖掘 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] G206[自动化与计算机技术—计算机科学与技术]