Wireless network data missing attribute recovery method and device based on graph neural network

Abstract

The invention discloses a wireless network data missing attribute recovery method and device based on a graph neural network. The method comprises the following steps: mapping wireless network data into a corresponding topological graph structure, and sequentially mapping sample data with missing attributes into attribute vectors of nodes in the topological graph structure; obtaining an adjacent matrix of the topological graph structure according to the attribute vector of the node; simplifying the topological graph structure by using a graph sampling algorithm to obtain a sparse adjacent matrix; based on the attribute vector and the sparse adjacent matrix, learning by using a graph neural network model, and outputting the attribute vector recovered after reconstruction. According to the method, an attribute recovery framework based on a graph automatic encoder is used, a graph neural network learning algorithm based on strategy gradient is adopted, modeling and learning are directly carried out on the attribute recovery problem of the wireless network data, the correlation in the wireless network data is fully utilized, and therefore, the attribute performance of wireless network data recovery is improved.

Description

technical field [0001] The invention relates to the problem of attribute recovery of wireless network data, in particular to a method and device for recovering missing attributes of wireless network data based on a graph neural network. Background technique [0002] Machine learning and deep learning have achieved great results in the past few years. Although new technological breakthroughs continue to emerge, the vast majority of supervised learning methods still require datasets with complete information. At the same time, many real-world problems still need to deal with datasets with incomplete information, such as biomedical or insurance sectors or financial institutions. Therefore, operations that need to complete those incomplete datasets are an essential and fundamental part of machine learning. [0003] The object of the attribute recovery algorithm is some data sets with missing data, and the missing part is replaced with the predicted value of the algorithm by usi...

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Problems solved by technology

[0008] Purpose of the invention: In view of the above problems, the present invention proposes a method for recovering missing attributes of wireless network data based on a graph neural network, which can fundamentally solve the problem of existing wireless network data The attribute restoration algorithm cannot effectively utilize the dependence of relevant attribute information, which leads to the problem that the performance of restoring attributes is not high enough

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