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Time link prediction method and device for dynamic weighted network, apparatus and medium

A dynamic weighting and time chaining technology, applied in the computer field, can solve problems such as the inability to realize dynamic weighting network time chain prediction, and achieve the effect of improving accuracy and effect

Inactive Publication Date: 2019-08-16
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to provide a time link prediction method, device, equipment and medium of a dynamic weighted network, aiming to solve the problem that the prior art cannot realize the time link prediction of a dynamic weighted network

Method used

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  • Time link prediction method and device for dynamic weighted network, apparatus and medium
  • Time link prediction method and device for dynamic weighted network, apparatus and medium
  • Time link prediction method and device for dynamic weighted network, apparatus and medium

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Embodiment 1

[0024] figure 1 The implementation flow of the time link prediction method of the dynamic weighted network provided by Embodiment 1 of the present invention is shown. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0025] In step S101, the network topology graph of the dynamic weighted network under continuous historical time stamps is obtained.

[0026] The embodiments of the present invention are applicable to data processing equipment, such as a computer, a server, or a server cluster composed of multiple servers.

[0027] In the embodiment of the present invention, the network topology of the dynamic weighted network changes with time, including changes in the properties of network vertices, the number of network vertices and the relationship of edge connections over time. For the new vertex that appears in the next future timestamp, the network topology of the historical ti...

Embodiment 2

[0036] figure 2 It shows the implementation process of the time link prediction method of the dynamic weighted network provided by the second embodiment of the present invention. For the convenience of explanation, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

[0037] In step S201, the network topology graph of the dynamic weighted network under continuous historical time stamps is acquired.

[0038] In this embodiment of the present invention, for the specific implementation of step S201, reference may be made to the detailed description of step S101 in Embodiment 1, which will not be repeated here.

[0039] In step S202, feature extraction is performed on the obtained network topological graph under each historical time stamp through the attention graph convolutional network to obtain comprehensive network features of the network topological graph under each historical time stamp.

[0040] In the embodiment of...

Embodiment 3

[0064] image 3 The structure of the time link prediction device of the dynamic weighting network provided by the third embodiment of the present invention is shown, and for the convenience of description, only the parts related to the embodiment of the present invention are shown.

[0065] A historical network diagram acquisition module 31, configured to acquire the network topology diagram of the dynamic weighted network under continuous historical time stamps; and

[0066] The network map prediction module 32 is used to input the network topology map under the historical time stamp into the pre-trained generation confrontation network, and predict the network topology map of the dynamic weighted network under the time stamp to be predicted. The generation confrontation network includes a generation model and a discriminant Models, generative models include attention graph convolutional networks and augmented attention LSTM networks that fuse temporal matrix factorization, a...

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Abstract

The method is suitable for the technical field of computers, and provides a time link prediction method for a dynamic weighted network. The method comprises the following steps of obtaining a networktopological graph of the dynamic weighted network under the continuous historical time stamps; inputting the network topological graph under the historical timestamp into a pre-trained generative adversarial network; predicting to obtain a network topological graph of the dynamic weighted network under the to-be-predicted timestamp, wherein the generative adversarial network comprises a generativemodel and an adversarial model, and the generative model comprises an attention map convolutional network and an enhanced attention long-short-term memory network fusing the time matrix decomposition, an attention mechanism and a long-short-term memory network, thereby realizing the time link prediction of the dynamic weighted network, and improving the time link prediction accuracy and effect ofthe dynamic weighted network.

Description

technical field [0001] The invention belongs to the field of computer technology, and in particular relates to a method, device, device and medium for time link prediction and updating of a dynamic weighted network. Background technique [0002] Link prediction (Link Prediction) refers to the prediction of the possibility of a link between two nodes in the network that have not yet produced a connection edge through known network nodes and structural information. Link prediction is used in dynamic network analysis for mining and analyze network evolution. Generally, link prediction can be divided into structural link prediction and temporal link prediction. Structural link prediction only considers the network structure of a single network snapshot and predicts possible future links within the same network. Time link prediction is to try to construct the network snapshot of the next timestamp when given the network snapshot of the previous timestamp. [0003] Traditional ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24H04L12/26G06N3/04
CPCH04L43/08H04L41/12G06N3/049G06N3/045
Inventor 曲强杨敏陈磊
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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