Community search schemata and their classification systems based on attribute graphs
At present,there are many community search methods in the attribute graph,and there is no systematic classi-fication method,which restricts the application of community search.In order to clarify the category of community search in attribute graph,the classification method of attribute community search is studied.Firstly,the concept of at-tribute community search schema is proposed to analyze the relationship between attribute community search schemata in depth,proposing four relationships of community search mode of attribute graph:equivalence,affiliation,intersected and exclusion.Secondly,a two-layer classification system is constructed based on the input graph attributes of the search mode,the topology of the output graph and the practical significance of the search mode of each attribute com-munity.The first layer is a family of sets composed of the same set of schemata in the input attribute graph.The input at-tribute graph here includes sequence,space,keyword,weight,and empty attribute graph.The second layer is each spe-cific community search schema located by the topology and practical meaning of the output graph.Then,the comparat-ive analysis result of corresponding community search algorithm is given for each schema in the second layer.Finally,the characteristics of all the community search modes of attribute graphs are analyzed centrally.Overall,the attribute graph community search pattern not only provides a powerful tool for understanding and analyzing complex network structures,but also provides a new perspective and method for solving practical problems.
万方数据
维普
