基于社区结构的科研合作关系分析与预测  

Collaboration analysis and prediction in scientific research network based on community structure

作　　者：伍杰华[1,2] 朱岸青[1,3] 

机构地区：[1]广东工贸职业技术学院计算机工程系,广州510510 [2]华南理工大学计算机科学与工程学院,广州510641 [3]暨南大学信息科学与技术学院,广州510632

出　　处：《计算机应用研究》2016年第3期701-705,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61003045);广东省教育部产学研结合项目(2012B091100043)

摘　　要：科研合作关系分析和预测针对网络的结构信息预测未来哪些学者间会产生合作关系进行研究,对于理解网络信息传播和动态变化具有重要的意义。在主流的基于拓扑属性的关系预测算法基础上提出了一种基于社区结构信息的合作关系预测模型。首先分析社区发现算法下科研网络的链接分布规律及给出模型构建的理论依据,然后构造引入社区拓扑结构信息的改进算法,最后采用不同社区发现算法进行实验。该方法在实验效果和性能上要优于一些经典的算法,说明该算法能够有效地引入社区结构信息对真实的科研合作网络关系预测问题建模,并为科研合作关系分析预测这一问题提供一种新的思路。Collaboration relation analysis and prediction aims to predict collaboration between scholars according to the network＇s structural information. It is of great significance to information diffusion and dynamic changes. This paper proposed a new algorithm based on the community structure to improve the classic prediction metric which incorporating the topological properties. Firstly,it gave links concurrent distribution under community detection algorithm in collaboration network and theoretical basis for model construction,and then proposed an improved predicting model based on community topology information. Finally,it used different community discovery algorithms for extensive experiment. The experimental results show that the effect and performance of this method is better than some classical algorithms. Moreover,such algorithm can effectively introduce community structure information on the collaboration prediction in real scientific research network and provide a new idea for analysis and forecast of such prediction problem.

关 键 词：科研网络 社区结构 合作关系预测 关系分析 社区信息 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]