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Link prediction method based on dynamic network attribute representation

A prediction method and dynamic network technology, applied in the field of data processing, can solve the problem of ignoring the influence of node preference information, and achieve the effect of enriching node relationship attribute information and realizing link prediction.

Pending Publication Date: 2022-03-08
公安部户政管理研究中心
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Problems solved by technology

And most of the existing methods only consider evenly spaced time intervals, while ignoring the influence of different time intervals on node preference information

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  • Link prediction method based on dynamic network attribute representation
  • Link prediction method based on dynamic network attribute representation
  • Link prediction method based on dynamic network attribute representation

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Embodiment Construction

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0065] figure 1 It is a flowchart of an embodiment of the present invention, such as figure 1 As shown, this embodiment provides a link prediction method based on dynamic network attribute representation, which includes the following steps:

[0066] Step S1: On the basis of the relational network data set, dynamically generate new links to the network data of the personnel subject database, and sort the newly generated links of the nodes according to the time...

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Abstract

The invention discloses a link prediction method based on dynamic network attribute representation, and the method comprises the steps: S1, dynamically generating new links of network data of a personnel theme library on the basis of a relational network data set, and sorting the links newly generated by nodes according to timestamps to obtain a link sequence; s2, a random walk algorithm based on connection strength is adopted to diffuse network node information of the personnel theme library, and a network model is formed; s3, updating node vector parameters of the network model by adopting a gradient descent method; s4, node feature learning based on a network structure is carried out on the personnel theme library network through an attribute network link prediction algorithm; s5, node attribute-based node feature learning is carried out on the personnel theme library network through an attribute network link prediction algorithm; and S6, carrying out feature fusion and attribute network link prediction on the node features based on the network structure and the node features based on the node attributes.

Description

technical field [0001] The invention relates to data processing technology, in particular to a link prediction method based on dynamic network attribute representation. Background technique [0002] Personnel subject database is an abstract concept that integrates, classifies, analyzes and utilizes data in information systems at a high level. In the process of building the personnel subject database, there are a large number of relational data of different dimensions, and their relationships are complicated. Associating complex data into a network structure for chain prediction, thereby mining hidden information can greatly improve information utilization. Link prediction is an important application in network analysis. Link prediction is mainly based on the known network to predict the hidden links in the network, or based on the current network to predict the links that will be generated in the future, so that it can be multi-angle, multi-faceted, Obtain information at m...

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Application Information

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IPC IPC(8): G06F16/901G06Q50/00G06N3/04G06N3/08
CPCG06F16/9024G06Q50/01G06N3/04G06N3/08
Inventor 黄双全刘威张鹏张照星黄潭龙施一琳范英康凯徐平徐飞陈洁徐骁高乾坤宰旭昕许广文
Owner 公安部户政管理研究中心
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