Knowledge Flow And Marketing Pricing In The Network Formal Context

As complex networks penetrate into various fields,knowledge acquisition in directed networks is an important direction in the field of network data analysis.As a powerful tool for data analysis,concept cognitive learning in the formal context is widely used in data mining,information retrieval and other fields.Combining concept cognitive learning in formal context with directed network analysis to construct a data framework and theoretical foundation between the two theories will be a very meaningful research direction.This paper mainly conducts the following two aspects of research:1、Combining formal concept cognitive learning and complex network analysis,how to obtain the directed structural matrix between node attributes from the directed structural matrix between nodes is studied,so as to predict the directed flow between concept connotations.This is a directed matrix transformation under the network formal context.Based on the directed structure matrix between nodes,it transforms the directed relation matrix between the connotation attributes of nodes.The specific ideas are as follows:(1)Based on the weighted directed network formal context,the knowledge flow operator is proposed,and the directed relationship between nodes is shifted to the directed relationship between its connotation attributes.(2)The directed attribute network is obtained from the knowledge flow operator and combined with the formal context to define the knowledge flow network formal context,knowledge flow matrix and knowledge flow cluster.(3)Further study the network eigenvalues of the knowledge flow cluster in the network to describe the average influence and the influence difference of the knowledge flow clusters in the network.(4)Combined with the knowledge flow matrix,the knowledge in-degree and out-degree matrix construction algorithm,the knowledge flow matrix construction algorithm are proposed,and the improved link prediction method and link value prediction method are proposed for the knowledge link value prediction problem.(5)The proposed algorithm is tested by using the dataset downloaded from the Web of Science core journal library,and experiments show the effectiveness of the proposed algorithm,and the results are verified by the network eigenvalues of the cold and hot knowledge flow clusters.2、The formal concept cognitive learning theory is applied to the construction of homogeneous marketing networks,and the pricing problem under homogeneous marketing networks is further studied,so as to obtain higher profits.The specific ideas are as follows:(1)The homogeneity of consumer is analyzed by the consumer’s rating preference,so as to build a homogenous marketing network,in which commodities are divided into mass or personalized products.(2)In homogeneous marketing networks,a two-stage pricing-consumption game model is established based on the consumer rating formal context to solve the optimal pricing problem.(3)According to the network structure and node attribute information,the pricing simulation is carried out in combination with the Independent Cascade Model,and the influence of each parameter on the pricing results is analyzed.The study provides a basis for monopolies to make pricing decisions about social networks when faced with homogeneous networks.