Data processing method and device based on graph convolutional neural network, equipment and medium

A convolutional neural network and data processing technology, applied in image data processing, neural learning methods, biological neural network models, etc., can solve the problems of inaccurate mining and inaccurate graph structure data processing results.

Pending Publication Date: 2020-07-28
WEBANK (CHINA)
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Problems solved by technology

[0005] The main purpose of the present invention is to provide a data processing method, device, equipment and medium based on a graph convolutional neural network, aiming to solve the problem t

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  • Data processing method and device based on graph convolutional neural network, equipment and medium
  • Data processing method and device based on graph convolutional neural network, equipment and medium
  • Data processing method and device based on graph convolutional neural network, equipment and medium

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Embodiment Construction

[0043] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0044] like figure 1 as shown, figure 1 It is a schematic diagram of the device structure of the hardware operating environment involved in the solution of the embodiment of the present invention.

[0045] It should be noted that the data processing device based on the graph convolutional neural network in the embodiment of the present invention may be a smart phone, a personal computer, a server, etc., and no specific limitation is made here.

[0046] like figure 1As shown, the graph convolutional neural network-based data processing device may include: a processor 1001 , such as a CPU, a network interface 1004 , a user interface 1003 , a memory 1005 , and a communication bus 1002 . Wherein, the communication bus 1002 is used to realize connection and communication between these components. The user interface 100...

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Abstract

The invention discloses a data processing method and device based on a graph convolutional neural network, equipment and a medium. The method comprises the steps of obtaining first graph structure data to be processed and inputting the first graph structure data to the pre-trained graph convolutional neural network; calling the graph convolutional neural network to perform neighbor node sampling operation and neighborhood information aggregation operation on each first node in the first graph structure data so as to update each first node; and calling the graph convolutional neural network toobtain a processing result of the first graph structure data based on the updated first nodes. According to the method, the difference of the relationship between the nodes is mined by the graph convolutional neural network, so that a more accurate processing result can be made according to mined more sufficient information.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, in particular to a data processing method, device, equipment and medium based on a graph convolutional neural network. Background technique [0002] With the development of computer technology, more and more technologies (big data, distributed, blockchain, artificial intelligence, etc.) The industry's security and real-time requirements also put forward higher requirements for technology. [0003] In recent years, deep learning has set off a technological revolution in industry and academia, and promoted the development of image detection and recognition, natural language processing, and speech recognition. At present, how to apply deep learning technology, especially the widely used convolutional neural network, to complex and high-dimensional graph structure data (such as social network and protein structure data, etc.) points of interest. [0004] Neural Message Passing repres...

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/08
CPCG06T7/0002G06N3/08G06T2207/10004G06T2207/20081G06N3/045
Inventor 张杰徐倩
Owner WEBANK (CHINA)
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