Method for solving influence maximization problem based on user behavior propagation model

Abstract

The invention discloses a method for solving an influence maximization problem based on a user behavior propagation model. According to the method, the individual user influence is calculated based onuser behaviors of the social network, the influence propagation probability is calculated based on the individual user influence, and the maximization scope of influenced users in a special social circle is calculated based on the influence propagation probability. Compared with a propagation model based on the network topology structure, a more considerable influence node set can be more easilyacquired in the social network, nodes with the larger influence can better influence other adjacent nodes, the success probability is correspondingly larger, the individual user influence can be solved through a PageRank method based on the time distribution user liveness to effectively eliminate zombie nodes, compared with a PageRank method based on the network topology structure, the acquired influence has greater timelines and accuracy, ranking of active users can be better improved, and ranking of inactive users can be reduced.

Description

technical field [0001] The present invention relates to the technical field of user behavior analysis and mining, in particular to a method for solving the influence maximization problem based on a user behavior propagation model. Background technique [0002] As the current mainstream online communication platform, social network has penetrated into the life and work of people from all walks of life. The data generated by user information, user behavior, and user relationship have immeasurable value. With the rapid development of social network, its scale is getting bigger and bigger, the number of nodes is numerous, the relationship between nodes is intricate, and the behavior data is huge. As the mainstream online communication platform, social network has penetrated into the life and work of people from all walks of life. The data generated by user information, user behavior, user relationship, etc. has immeasurable value hidden. With the rapid development of social net...

AI Technical Summary

Problems solved by technology

The relationship between web pages and the relationship between users in social networks is actually very similar. From the perspective of graph theory, social relationship networks and Web networks have similar topological structures, but users in social networks are Conscious individuals, different users will have their own different behavior habits, and will generate a lot of data, and the web pages are basically hung up, and the data is relatively single

If the PageRank algorithm is directly used to calculate the influence of social network users, it only considers the attention relationship between users and ignores some factors of the user's own behavior, which cannot objectively reflect the real influence of users