Graph-based convolutional network training method, device and system

Abstract

The invention discloses a graph-based convolutional network training method, device and system. The method comprises the following steps: establishing a first storage space for each layer, except thelowest layer and the highest layer, of a convolution model; based on the training data and the graph of each batch, determining each center node of the training data and each neighbor node of the center node; for each center node, obtaining a representation vector of each neighbor node from a first storage space corresponding to each neighbor node identifier of the previous layer of center node; according to the characterization vector of the central node transmitted from the previous layer and the obtained characterization vector of each neighbor node, determining a representation vector of the central node in the current layer, and when the current layer is not the lowest layer or the highest layer, transmitting the determined representation vector to the next layer adjacent to the current layer and updating the representation vector corresponding to the central node identifier in the first storage space of the current layer until the representation vector of the center node at the highest layer is obtained. According to the invention, the training calculation amount and training time can be effectively reduced.

Description

technical field [0001] The present invention relates to the technical field of machine learning, in particular to a graph-based convolutional network training method, device and system. Background technique [0002] With the popularization of mobile terminals and application software, service providers in the fields of social networking, e-commerce, logistics, travel, food delivery, and marketing have accumulated massive business data. Based on the massive business data, mining the relationship between different business entities (entities) Relationship has become an important technical research direction in the field of data mining. With the improvement of machine processing capabilities, more and more technicians have begun to study how to mine through machine learning technology. [0003] The inventors of the present invention have found that: at present, learning massive business data through machine learning technology to obtain a graph (Graph) for expressing the relat...

AI Technical Summary

Problems solved by technology

[0006] It is easy to see that the amount of calculation of this type of method increases exponentially with the increase of the number of layers. With the deepening of the application, for a graph with a large number of nodes, the overall training time of the model can be unacceptable

[0007] The demand for high computing power increases the training time of the GCN model, which restricts the application of the GCN model in various practical applications such as search, advertising, recommendation, social network mining, etc.

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