基于多关系网络的话题意见领袖挖掘  

Topic Opinion Leader Mining Based on Multi-relational Networks

作　　者：段震 倪云鹏[1,2,3] 陈洁 张燕平[1,2,3] 赵姝[1,2,3] DUAN Zhen;NI Yunpeng;CHEN Jie;ZHANG Yanping;ZHAO Shu(Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education,Hefei 230601,China;School of Computer Science and Technology,Anhui University,Hefei 230601,China;Information Materials and Intelligent Sensing Laboratory of Anhui Province,Hefei 230601,China)

机构地区：[1]计算与信号处理教育部重点实验室,合肥230601 [2]安徽大学计算机科学与技术学院,合肥230601 [3]安徽省信息材料与智能传感重点实验室,合肥230601

出　　处：《数据采集与处理》2022年第3期576-585,共10页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61876001);国防科技创新特区项目(2017-0001-863015-0009)。

摘　　要：社交网络中的意见领袖在信息传播过程中起着重要的作用。传统的意见领袖挖掘仅基于网络结构,没有考虑特定话题或者事件下的作用,且目前基于话题的意见领袖挖掘仅基于单一的网络结构,并没有考虑到节点间的多种交互关系。本文提出一种基于多关系网络的话题意见领袖挖掘方法(Multi-relational networks,MRTRank),融合话题因素和节点间多种交互关系,通过一种属性网络表示学习算法,得到不同节点在多关系网络上的相似性,形成节点的转移概率矩阵,最终通过PageRank算法得到top-k个意见领袖。在真实Twitter数据集上的实验结果验证了本文提出的方法优于传统的意见领袖挖掘算法。Opinion leaders in social networks play an important role in the process of information dissemination.The traditional mining of opinion leaders is based on network structures and doesnot consider the role of a specific topic or event,and the current mining of opinion leaders based on topic is only based on a single network structure,without taking into account the multiple interactive relationships between nodes.This paper proposes a topic opinion leader mining method based on multi-relational networks(MRTRank),which joins topic factors and a variety of interactive relationship between nodes.Through an attribute network representation learning algorithm,the similarity of different nodes in the multi-relationship network is obtained,and the transition probability matrix of nodes is formed.Finally,the top-k opinion leaders are obtained through the PageRank algorithm.Experimental results on real Twitter datasets verify that the proposed method is superior to traditional opinion leader mining algorithms.

关 键 词：意见领袖 两级传播 社交网络 PAGERANK 属性网络表示学习 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]