Label noise-oriented graph node classification method
The invention discloses a label noise-oriented graph node classification method. The method comprises the following steps of: 1) constructing a plurality of graph convolutional network (GCN) models; 2) estimating joint distribution on the graph data, pruning noise data to obtain primarily cleaned da...
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Main Authors | , , |
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Format | Patent |
Language | Chinese English |
Published |
20.11.2020
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Subjects | |
Online Access | Get full text |
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Summary: | The invention discloses a label noise-oriented graph node classification method. The method comprises the following steps of: 1) constructing a plurality of graph convolutional network (GCN) models; 2) estimating joint distribution on the graph data, pruning noise data to obtain primarily cleaned data, and training a graph convolutional network model; 3) training a graph convolution network modelon the graph data, predicting unmarked nodes together with the graph convolution network model in the step 2) to obtain a pseudo label and a prediction probability matrix, and jointly estimating full-data joint distribution and pruning noise data in combination with the label with the marked nodes and the prediction probability matrix; and 4) training a graph convolutional network model by using the graph data subjected to secondary cleaning obtained in the step 3), and predicting a node category. According to the invention, on the basis of a graph convolution network model, a noise joint distribution estimation and p |
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Bibliography: | Application Number: CN202010625468 |