Web社会网络的粗糙属性图模型及应用

针对Web环境下的社会网络具有信息粗糙性的特征，即Web数据中有大量垃圾内容和垃圾链接，同时很多信息是不完整的、缺失的,且信息有重复现象存在等，在已提出的属性图模型基础上，结合粗糙集理论解决不完备信息的优势，首先提出粗糙顶点属性图和粗糙边属性图，进而给出粗糙属性图的概念，以对Web社会网络结构进行分析，使其能够描述复杂Web社会网络中的不完整信息以及动态变化的链接。其次对粗糙属性图的粗糙特性进行分析，给出粗糙顶点精度、粗糙边精度和粗糙图精度等概念，得出粗糙属性图的精度与顶点和边集属性划分程度有关的结论，即人们对图的认知程度与图的精度密切相关。最后，在中国知网上通过对论文作者进行查询得到粗糙图，并通过不断添加顶点属性，将图顶点划分得越来越精细，挖掘出要查询的作者合作关系图，从而说明粗糙属性图在社会网络分析中符合人们的认知过程。

The rough attribute graph model of Web social network and its application

The paper targets the rough information feature of the social network in Web environment, which is that the Web data has a tremendous amount of junk content and garbage link and lots of information is incomplete, missed and repeatable. Firstly, based on the proposed the attribute graph model and combing the advantages of rough set theory solving incomplete information, the rough vertex attribute graph and the rough edges graph are proposed. Furthermore, the rough attribute graph that can describe the complex Web of incomplete social network information and the dynamic changes of links is given so that the Web social network structure is analyzed in an even better fashion. Secondly, the rough characteristics of the rough attributes graph is analyzed in order to give the concepts of rough vertex accuracy, rough edge accuracy, and rough graph accuracy and obtain the conclusion that the accuracy of rough attributes graph is related to the division level of vertex and edge set property. In other word, people's cognition of graph is closely related to the accuracy of graph. Finally, the paper authors are queried to get the rough graph in Chinese HowNet. By constantly adding the vertex attributes and dividing the graph vertex, the author's cooperation relations figure is excavated, thus demonstrating that the rough attribute graph in social network analysis is in line with the cognitive processes of people.