Network representation learning method and device and storage medium

A network representation and network technology, applied in the field of network analysis, can solve the problem that the method of matrix decomposition cannot be applied, and cannot reasonably and effectively combine data from different sources, so as to reduce complexity, improve quality, and enhance effect. Effect

Pending Publication Date: 2019-09-10
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

However, in the era of data explosion, there are hundreds of millions of nodes in a large network structure. Due to the problem of computational complexity, the method ba

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  • Network representation learning method and device and storage medium
  • Network representation learning method and device and storage medium
  • Network representation learning method and device and storage medium

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Embodiment Construction

[0048] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0049] figure 1 A schematic flowchart of a network representation learning method provided by an embodiment of the present invention is given, as shown in figure 1 As shown, in this embodiment, a method for network representation learning includes the following steps:

[0050] Obtaining structural information of each node in the network, and establishing a structural transfer matrix based on the structural information;

[0051] Obtain attribute information of each node in the network, and establish an attribute transfer matrix based on the attribute information;

[0052] Fusing the structural transfer matrix and the attribute transfer matrix to obtain a biased transfer matrix;

[0053] Sampling each node in the b...

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Abstract

The invention discloses a network representation learning method and device and a storage medium, and the method comprises the steps of obtaining the structure information of each node in a network, and building a structure transfer matrix based on the structure information; obtaining the attribute information of each node in the network, and establishing an attribute transfer matrix based on theattribute information; fusing the structure transfer matrix and the attribute transfer matrix to obtain a bias transfer matrix; sampling each node in the bias transfer matrix to obtain a plurality ofwalking sequences; and establishing a neural network model, and inputting the walking sequence into the neural network model for network representation learning to obtain a representation vector of anode corresponding to the walking sequence. According to the present invention, the information of different sources can be seamlessly combined, the calculation complexity is reduced, and the networkrepresentation quality is improved, so that the effect of the representation vector on a network analysis task is enhanced.

Description

technical field [0001] The present invention relates to the technical field of network analysis, in particular to a method, device and storage medium for network representation learning. Background technique [0002] Network structure is a broad representation of data, and information networks have become ubiquitous in practical applications, such as social networks, citation networks, biological networks, etc. In the era of big data, the network has become an important medium for effectively storing and exchanging entity relationship knowledge. Mining knowledge in network data has attracted continuous attention from academia and business. [0003] At present, the research on network representation learning has developed from traditional matrix eigenvector calculation to deep learning algorithm based on random walk and network representation learning combined with external information. However, in the era of data explosion, there are hundreds of millions of nodes in a large...

Claims

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Application Information

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IPC IPC(8): G06N3/08G06N3/04
CPCG06N3/08G06N3/044
Inventor 蔡晓东刘玉柱
Owner GUILIN UNIV OF ELECTRONIC TECH
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