Link prediction method and system based on deep non-negative matrix factorization

A technology of non-negative matrix decomposition and link prediction, which is applied in the fields of instrumentation, computing, character and pattern recognition, etc., can solve the problem of link prediction methods that are difficult to obtain performance, save computing and storage resources and time, and enrich network structure information , the effect of improving the generalization performance

Active Publication Date: 2020-03-03
SHANDONG JIANZHU UNIV
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Problems solved by technology

Due to the limited information and the sparsity of the network, it is difficult for traditional link prediction methods to achieve good performance

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  • Link prediction method and system based on deep non-negative matrix factorization
  • Link prediction method and system based on deep non-negative matrix factorization
  • Link prediction method and system based on deep non-negative matrix factorization

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[0080] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0081] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

[0082] The relationship between nodes in a complex network depends not only on the topological properties of the...

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Abstract

The invention discloses a link prediction method and system based on deep non-negative matrix factorization. The method comprises the following steps: (1), giving a network adjacency matrix accordingto the link relation between to-be-predicted network nodes; (2) a pre-training stage: carrying out non-negative matrix decomposition on the network adjacent matrix to obtain a basis matrix and a coefficient matrix, and then carrying out non-negative matrix decomposition on the coefficient matrix for a plurality of times so as to decompose the network adjacent matrix into a form of multiplying a plurality of basis matrixes by one coefficient matrix; a fine adjustment stage: establishing a loss function based on the network adjacency matrix, a plurality of basis matrixes and a coefficient matrix, judging whether a loss function value is smaller than tolerance or not, If so, entering the step (3); if not, finely adjusting the basis matrix and the coefficient matrix; re-judging whether the loss function value is smaller than the tolerance; (3) calculating a network similarity matrix according to the finely tuned basis matrix and the finely tuned coefficient matrix; and realizing link prediction according to the network similarity matrix.

Description

technical field [0001] The invention relates to a link prediction method and system based on deep non-negative matrix decomposition. Background technique [0002] Link prediction is one of the research hotspots in complex networks in recent years, it can help us explore and understand the evolution mechanism of complex networks. Link prediction is to predict the links between existing but unobserved nodes in the network, or to predict the future links between the current nodes of the network. [0003] Currently, existing link prediction methods in complex networks can be divided into two categories. The first type of method based on node similarity believes that the greater the similarity between two nodes, the more likely there is a link between them. It only depends on the network topology, but its predictive ability is limited. The second category of methods is based on statistical analysis and probability theory. These methods usually assume that the network has a kno...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2133G06F18/22G06F18/214
Inventor 蔡菲牟晓慧陈杰张鑫李鲁锋姚国标
Owner SHANDONG JIANZHU UNIV
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