摘要
在舆情分析、微博营销和个性化推荐等方面,微博社区发现的研究都具有重要的应用价值。为了准确而有效地发现微博社交网络中的社区,提出一种基于信任关联度的微博社区发现算法(TRKM算法)。该算法通过微博用户的评论、转发、原创微博等属性来构造节点间信任关联度,再利用微博社区的模块度对网络社区划分效果进行评价。在新浪微博明星和普通用户数据集上进行实验,并将TRKM算法与传统K-means算法作比较。实验表明,该算法能够更有效地发现微博用户关系网络中的社区结构。
The research on micro-blog community detection has important application value in public opinion analysis, microblog marketing and personalized recommendation, etc. In order to find communities in micro-blog social networks accurately and efficiently, this paper proposes a micro-blog community detection algorithm based on trust relation degree (TRKM algorithm). This algorithm constructs the trust relation degree between the nodes through user comments, forwarding number, original micro-blog article number and other attributes, and uses the module degree of micro-blog community to evaluate the effects of network community partition. Experiments are carried out respectively on the Sina micro-blog dataset of stars and ordinary users to compare TRKM algorithm with the traditional K-means algorithm. Experimental result indicates that TRKM algorithm can more effectively find the community structure in mirco- blog user relationship networks.
作者
杨长春
刘玲
李雪佳
吕晨
顾寰
Yang Changchun Liu Ling Li Xuejia Lu Chen Gu Huan(School of Information Science and Engineering, Changzhou University, Changzhou 213164, Jiangsu, China)
出处
《计算机应用与软件》
2017年第3期194-198,271,共6页
Computer Applications and Software
基金
国家自然科学基金项目(61272367)
江苏省产学研前瞻性联合研究项目(BY2014037-08)
关键词
微博网络
社区划分
TRKM算法
信任关联度
社区模块度
Micro-blog networks Community partition TRKM algorithm Trust relation degree Community module degree