Social network based mobile terminal user grouping method

Abstract

The invention discloses a social network based mobile terminal user grouping method. The method comprises: according to history of communication between terminal users, quantizing communication contact to generate a social relational graph (STG); in combination with preference attributes of the terminal users, generating an attribute relational graph (ARG) taking preference degrees between the terminal users and attributes as weights; generating a social relation-attribute graph in combination with the STG and the ARG, designing an SAPLA algorithm to predict unknown attributes of the terminal users, and adjusting preference degrees of known attributes; and proposing an SARA algorithm by utilizing a random walk model, combining transfer probabilities between the terminal users and between the terminal users and the attributes, giving out a transfer probability matrix between the terminal users, with relatively low complexity, giving out a random walk distance matrix Rl by utilizing the transfer probability matrix, setting a target function in combination with the matrix Rl, and grouping the terminal users until the target function is converged. According to the method, the complexity of operation is lowered and the accuracy of grouping is improved.

Description

technical field [0001] The present invention relates to the field of mobile communication technology and data mining. More specifically, the present invention relates to a social network-based method for grouping mobile terminal users, which can effectively mine user attributes and relationships among users, and perform reasonable grouping. Background technique [0002] Due to the accumulation and generation of big data in the real world, more and more research results on the network have been discovered and utilized by people, which provides people with a deeper understanding of various complex network systems in the real world, and the control or application of these systems Provided effective help. Social networks can reflect social members and their mutual relationships, but how to dig out the invisible relationships hidden under the surface relationships through the analysis of social networks, and group users according to these relationships, is very important for opti...

AI Technical Summary

Problems solved by technology

[0005] In the existing technical solutions, the weight between users is not set according to the historical behavior of end users in the mobile communication network scene, and the influence between users cannot be well reflected. Attribute prediction is not comprehensive enough to predict that users don’t like something, and in the traditional community grouping method, the transition probability matrix between users generated according to the random walk model has high computational complexity, which greatly reduces the feasibility of the end user grouping method sex and practicality

Images