Method and device for training graphic neural network model

A neural network model and network graph technology, applied in biological neural network models, neural learning methods, etc., can solve the problems that machines cannot accommodate data and the training efficiency of GNN models is low, so as to reduce machine requirements and improve training efficiency.

Active Publication Date: 2020-01-17
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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Problems solved by technology

[0003] Due to the large scale of graph data, for example, it can reach a scale of one billion nodes and one hundred billion edges. Common machines cannot acco

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  • Method and device for training graphic neural network model
  • Method and device for training graphic neural network model
  • Method and device for training graphic neural network model

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[0045] The following describes the solutions provided in this specification with reference to the drawings.

[0046] figure 1 This is a schematic diagram of an implementation scenario of an embodiment disclosed in this specification. This implementation scenario involves the training of the graph neural network model. Specifically, the graph neural network model is trained using a pre-established relational network diagram. The relational network graph includes multiple nodes and connecting edges between the nodes, each node has its own corresponding node number, and each connecting edge has its own corresponding edge number. To figure 1 Take the relationship network diagram in as an example. The relationship network diagram includes node 11, node 21, node 22, node 23, node 31, node 32, node 33, node 34, node 35, node 41, and between node 11 and node 21. There is a connecting edge, but there is no connecting edge between the node 21 and the node 23, the edge number of the connec...

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Abstract

The embodiment of the invention provides a method and a device for training a graphic neural network model. The method comprises the following steps: acquiring a target training sample and a corresponding target sample label from a sample set; wherein the target training sample corresponds to a target node in a target relationship network graph, the target node has a target node number, the targetrelationship network graph comprises a plurality of nodes and connecting edges between the nodes, each node has a respective corresponding node number, and each connecting edge has a respective corresponding edge number; querying graph information of a target sub-graph of the target relational network graph from pre-stored graph information of the target relational network graph according to thetarget node number and preset parameters; wherein the target sub-graph takes the target node as a central node, and the hop count between each node in the target sub-graph and the target node is lessthan or equal to a preset parameter; and training the graph neural network model by using the graph information of the target sub-graph and the target sample label. Requirements on a machine can be reduced, and training efficiency can be improved.

Description

technical field [0001] One or more embodiments of this specification relate to the computer field, and in particular to a method and device for training a graph neural network model. Background technique [0002] Graph data is a data structure representing connection relationships between people or things. In production and life, it is often necessary to mine information in graph data in order to improve production efficiency or improve human experience. The graph neural network (GNN) algorithm is an industry-leading algorithm for mining information in graph data. [0003] Due to the large scale of graph data, for example, it can reach a scale of one billion nodes and one hundred billion edges. Common machines cannot accommodate such large-scale data. Although there are machines that can accommodate such large-scale data, the training efficiency of the GNN model Also very low. [0004] Therefore, it is hoped that there will be an improved solution that can reduce the requ...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/08
Inventor 葛志邦黄鑫王琳
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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