Research And Implementation Of Credit Evaluation Model Based On Social Network

The rapid development of Internet technology makes social network has become an important means of communication between people.The virtual world and the real world have been mingled with each other and influence each other,and the face-to-face relationship between people has extended to the network.Social network has brought convenience and interest to people's lives,but also caused a lot of problems and threats.Social dishonesty occurs from time to time,more and more people pay attention to the credit situation of social users.How to monitor the network environment,regulate and restrain the behavior of Internet users is a major problem that needs to be faced.Based on the existing credit evaluation model of credit evaluation model,it is found that the single credit evaluation model has matured and it is difficult to break through and expand.Moreover,many studies have proved that the single credit evaluation model is prominent.Through the integrated learning,model is complementary,greatly improve the prediction accuracy and stability of a single model and other properties,but when the number of single model is too large,there will be too long sample learning time,resulting in a decline in credit evaluation efficiency,but also increased the machine storage space requirements.To solve these problems,the idea of selective integration has been put forward.Selective ensemble method has been applied to many fields,and have achieved good results,but in the field of personal credit evaluation,research is still relatively small,so the application of selective integration method in the field of personal credit assessment has a very big development space.In view of the network integrity problem existing in the social network,this paper takes Sina and micro-blog as the research object,and introduces the idea of selective integration into the field of micro-blog credit evaluation.The main contents of this paper are as follows:(1)Combining the characteristics of micro-blog platform and the problems existing inthe existing evaluation index system,the credit evaluation index system was rebuilt,and the validity of the index system was proved through comparative experiments;(2)Introducing the idea of selective ensemble learning into the field of micro-blog user credit evaluation,a KGSO selective ensemble algorithm based on K-means clustering and swarm optimization is proposed;(3)The KGSO selective integration algorithm is applied to micro-blog user credit evaluation,and the effectiveness and superiority of KGSO algorithm are verified by comparative experiments;...