Heterogeneous information network embedding method based on edge sampling

A technology of heterogeneous information network and neural network, which is applied in the field of heterogeneous information network embedding based on edge sampling, which can solve problems such as inability to handle weighted graphs and restricted walking.

Active Publication Date: 2021-05-07
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Application Information

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Problems solved by technology

[0006] Aiming at the above-mentioned deficiencies in the prior art, a heterogeneous information network embedding method based on edge sampling provided by the present invention solves the problems that existing graph embedding algorithms cannot handle weighted graphs and limited walks

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  • Heterogeneous information network embedding method based on edge sampling
  • Heterogeneous information network embedding method based on edge sampling
  • Heterogeneous information network embedding method based on edge sampling

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Embodiment Construction

[0040] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.

[0041] Such as figure 1 As shown, a heterogeneous information network embedding method based on edge sampling includes the following steps:

[0042] S1. Preprocess the weight of each edge in the intelligence data set to obtain the walking weight;

[0043] The formula for calculating the walking weight in step S1 is:

[0044]

[0045]

[0046] Among them, w′ is the walking weight, a p,q (n pre ,n next ) is the wal...

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Abstract

The invention discloses a heterogeneous information network embedding method based on edge sampling, and the method comprises the following steps: S1, carrying out the preprocessing of the weight of each edge in an information data set, and obtaining a migration weight; S2, selecting the ith information element node from the information element node set as a migration starting point, and carrying out multiple migration with a rated length on the migration starting point based on edge sampling to obtain a migration array; S3, repeating the step S2 until i is equal to the total number of the nodes in the intelligence element node set V, namely '2jeemaa2' V '2jeemaa2', and constructing a plurality of walk arrays into a walk array set; S4, selecting one migration array in the migration array set, constructing a training sample based on the migration array, and training the single-hidden-layer neural network; and S5, inputting the intelligence element nodes in the intelligence element node set into the trained single-hidden-layer neural network to obtain an embedded vector. According to the method, the problems that the weighted graph cannot be processed and the migration is limited in the existing graph embedding algorithm are solved.

Description

technical field [0001] The invention relates to the technical field of data security, in particular to a heterogeneous information network embedding method based on edge sampling. Background technique [0002] In the prior art, there are few methods for hierarchical classification of intelligence data, and there are only methods for dividing according to user attributes or specific behaviors. This type of method depends on user attributes and behaviors, and the scalability is not strong. In the prior art, there is a method of intelligence analysis. In the push method based on content similarity, keywords and expert scores are used to establish a user interest model, and the recommendation is made after calculating the similarity. The essence is still the content of the basic matrix decomposition, and the disadvantage is that it cannot be solved. The problem of matrix sparsity; the method based on the retrieval system uses different international and domestic online joint ret...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N1/32G06N3/08
CPCH04N1/32149H04N1/32288G06N3/08Y02D30/70
Inventor 王梦惟利强潘晔王沙飞邵怀宗林静然
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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