查询结果:   刘大海,张博锋,邹国兵,顾程伟.微博用户模型复杂网络中多维有向社区发现[J].计算机应用与软件,2016,33(7):129 - 133.
中文标题
微博用户模型复杂网络中多维有向社区发现
发表栏目
网络与通信
摘要点击数
554
英文标题
MULTI-DIMENSIONAL DIRECTED COMMUNITY DETECTION IN COMPLEX NETWORK OF MICROBLOGGING USER MODEL
作 者
刘大海 张博锋 邹国兵 顾程伟 Liu Dahai Zhang Bofeng Zou Guobing Gu Chengwei
作者单位
上海大学计算机工程与科学学院 上海 200444     
英文单位
School of Computer Engineering and Science,Shanghai University,Shanghai 200444,China     
关键词
用户模型 复杂网络 多维有向社区发现
Keywords
User model Complex networks Multi-dimensional directed community detection
基金项目
国家自然科学基金项目(61303096);上海市自然科学基金项目(13ZR1454600)
作者资料
刘大海,硕士生,主研领域:复杂网络,数据挖掘。张博锋,研究员。邹国兵,讲师。顾程伟,硕士生。 。
文章摘要
大多数社区发现是基于一种信息的,即从一个维度来划分社区。但在现实场景中,用户之间社区构成是受兴趣、社交关系、地域、教育背景等诸多因素共同影响形成的。这些多维信息有些是无向的,如兴趣相似度等;有些是有向的,如关注关系等。根据有向社区发现的原理,将多个维度的信息融合,提出一种面向多维复杂网络的有向社区发现(MDCD)算法。通过实验证明,MDCD算法相对于传统的多维社区发现方法AMM算法,社区发现结果准确率提高了17.7%、F-measure值提高了0.068;与一维的兴趣相似度网络进行对比,MDCD算法的三维复杂网络社区发现结果的准确率提高了36.1%、召回率提高了25.3%。由于多维有向社区发现综合考虑了多维的信息,得到的社区结构具有更重要的社会意义。
Abstract
Most of community detections are based on one kind of information,i.e.to partition the community using one dimension.However in reality scene,the composition of communities between users is formed by the combined effect of many factors,such as interests,social relationships,geography,educational background,etc.Moreover,some of these multi-dimensional information are undirected,for ex.,the similarity of interests,but some others,like the relationship of follow,are directed.Based on the principle of directed community detection,in this paper we fuse the multi-dimensional information and propose a multi-dimensional complex network-oriented directed community detection algorithm (MDCD).It is proved through experiment that the MDCD algorithm,relative to conventional multi-dimensional community discovery algorithm AMM,improves the accuracy of community detection result by 17.7% and the F-measure value by 0.068; Furthermore,by comparing the MDCD algorithm with the one-dimensional interests similarity network,it improves the precision rate of three-dimensional complex network detection result by 36.1% and the recall rate by 25.3%.Since the multi-dimensional directed community detection considers the multi-dimensional information comprehensively,the community structure obtained has more important social significance.
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