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Link prediction method based on local topology information and community correlation

A technology of local topology and link prediction, applied in digital transmission systems, electrical components, transmission systems, etc., can solve the problem of low prediction accuracy, and achieve the effect of improving prediction accuracy

Inactive Publication Date: 2017-10-13
XIDIAN UNIV +1
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Problems solved by technology

[0003] To sum up, the problem existing in the existing technology is that the prediction accuracy of the current link prediction method based on local topology information is not high

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  • Link prediction method based on local topology information and community correlation
  • Link prediction method based on local topology information and community correlation
  • Link prediction method based on local topology information and community correlation

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Embodiment Construction

[0039] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0040] The application principle of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0041] The present invention is performed in a complex network with no right and no direction. Use A to represent the adjacency matrix. If node i is connected to node j, then the value of row i and column j of A is A ij = 1, otherwise A ij =0. The degree of a node is defined as the number of all nodes directly connected to the node in the network, and the degree of node i is usually recorded as k i , you can use the formula express.

[0042] Definition: Γ(x) is the neighbor set ...

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Abstract

The invention belongs to the technical fields of network science and link prediction, and discloses a link prediction method based on local topology information and community correlation. The link prediction method based on local topology information and community correlation includes the following steps: the similarity value between nodes is calculated according to local topology information between the nodes; when the two nodes are in the same community, the community correlation reaches the maximum; when the two nodes are in different communities, on the basis of considering the local topology information, the similarity between the nodes is converted into the correlation between the two communities, and the correlation value between the two communities is calculated; and finally, link prediction is carried out by considering the influence of both the local topology information and the community correlation. The method avoids the problem that there are too many node pairs with similarity of 0 in the prediction result, and improves the accuracy of link prediction.

Description

technical field [0001] The invention belongs to the technical field of network science and link prediction, and in particular relates to a link prediction method based on local topology information and community correlation. Background technique [0002] Link prediction is one of the important bridges connecting complex networks with information science, and has important practical application value. Link prediction refers to predicting the possibility of a link between two unlinked nodes in the network based on information such as the network structure of the known network. Links include unknown and future links. Unknown links refer to links that exist in the current network but are missing for some reason. Future links refer to links that do not exist in the current network but may appear in the future. At present, the link prediction algorithm based on local topology information has become the mainstream method because of its low algorithm complexity and simple calculati...

Claims

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Application Information

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IPC IPC(8): H04L12/24
CPCH04L41/12H04L41/142H04L41/145H04L41/147
Inventor 杨清海席敏燕
Owner XIDIAN UNIV
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