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Data fusion method and device for distributed graph learning

A data fusion and distributed technology, applied in the computer field, to achieve the effect of improving efficiency

Active Publication Date: 2022-02-22
ALIPAY (HANGZHOU) INFORMATION TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

That is, the graph data is divided and stored on multiple devices, however, there may be associations between nodes distributed on different devices

Method used

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  • Data fusion method and device for distributed graph learning
  • Data fusion method and device for distributed graph learning
  • Data fusion method and device for distributed graph learning

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Embodiment Construction

[0026] The technical solutions provided in this specification will be described below in conjunction with the accompanying drawings.

[0027] Those skilled in the art can understand that graph data can generally include multiple nodes and connection relationships between nodes. Graph data can be expressed in the form of several triples such as (a, r, b), where a and b represent two nodes, and r represents the connection relationship between the two nodes. Graph data can be visualized in the form of a relational network or a knowledge graph, and the connection relationship between each node is represented by a connection edge.

[0028]In practice, each node in the graph data corresponds to each entity associated with a specific business scenario. For example, in the case that the specific business scenario is community discovery, user grouping, etc. related to users, each business entity corresponding to each node in the graph data may be, for example, a user. For another exa...

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Abstract

The embodiments of this specification provide a data fusion method and device for distributed graph learning, which are used for a distributed graph learning process for graph data through a distributed system, and a single device of the distributed system is pre-allocated with multiple graphs of graph data. Nodes and corresponding node connection relationships, wherein the first device includes N graph nodes and M mirror nodes, and a single mirror node and a single graph node in the N graph nodes are neighbor nodes to each other; During the fusion process, on the one hand, the first device performs fusion operations on the M mirror nodes through multiple independent mirror fusion threads, and respectively adds the mirror fusion vectors of the mirror nodes into the local aggregation data sequence, and on the other hand, uses the sending thread to sequentially perform fusion operations. The mirror fusion vector is sent so that the aggregation process of each mirror node is independent of each other. This method can improve the efficiency of data fusion in the process of distributed graph learning.

Description

technical field [0001] One or more embodiments of this specification relate to the field of computer technology, and in particular to a data fusion method and device for distributed graph learning. Background technique [0002] Graph data is a data form that describes the relationship between various entities. Graph data may generally include multiple nodes, and each node corresponds to each business entity. In the case that the business entity has a predefined association attribute, the corresponding nodes of the graph data may have a corresponding association relationship based on the association attribute. For example, in the graph data represented by several triples, the triple (a, r, b) indicates that there is an association relationship r between node a and node b. In the visualized graph data, node a and node b are represented by points, and the corresponding relationship r between node a and node b can be represented by connecting edges. Graph data can usually be ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50
CPCG06F9/5038G06N3/098G06N20/00G06N5/022
Inventor 郭志强
Owner ALIPAY (HANGZHOU) INFORMATION TECH CO LTD