Multi-attribute inference method of social network users based on variational automatic encoder

Abstract

A multi-attribute inference method of social network users based on a variational automatic encoder comprises the following steps: preprocessing online social network data, and constructing a user attribute network; constructing an attribute deduction model which comprises a user variation automatic encoder, an attribute variation automatic encoder and a discriminator, encoding input data by the model to obtain potential representation of the user and attribute information, and reconstructing a complemented user attribute matrix through the potential representation of the user; training a model through an adversarial training mode, so that the obtained potential representation of the user contains more complete attribute information; inputting the to-be-completed user attribute data and the friend relationship among the users into the model, wherein the output user attribute matrix represents the probability that the users have different attributes. The method can be used for complementing the user attribute data in the online social network, so that the complete user portrait is obtained, the required data is easy to obtain, the calculation complexity is low, the attributes can bequickly inferred in the complex network, and meanwhile, the accuracy in most attribute prediction is very high.

Description

technical field [0001] The invention belongs to the technical field of graph data mining, and in particular relates to a multi-attribute inference method for social network users based on a variational autoencoder (VAE). Background technique [0002] With the rapid development of Internet technology, online social networks such as QQ, Weibo, Facebook, Twitter, etc. have become an indispensable part of our lives. In order to obtain a better social experience, users often fill in some personal attribute information on these social platforms, such as gender, age, location, hometown, company, school, etc. This information constitutes a comprehensive description of a user, that is, a portrait . These attribute information are of great significance both for the research of social networks by relevant researchers and for the management and analysis of social platforms using such information. However, there are a lot of missing information in real social networks, so the attribute...

AI Technical Summary

Problems solved by technology

These methods often need to rely on prior knowledge to model the relationship between attributes and user connections. However, when there are many types and quantities of attributes, prior knowledge is often difficult to accurately describe this complex connection. Therefore, these traditional methods Difficult to achieve satisfactory results

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