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Link prediction method based on local community information

A link prediction and community technology, applied in digital transmission systems, electrical components, transmission systems, etc., can solve the problems of poor prediction effect and low reliability, and achieve the effect of good prediction effect.

Active Publication Date: 2015-12-16
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0005] In order to overcome the shortcomings of low reliability and poor prediction effect of the existing link prediction method based on local network topology information, the present invention proposes a link prediction method based on local community information with high reliability and good prediction effect

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  • Link prediction method based on local community information

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Embodiment Construction

[0027] The present invention will be further described below in conjunction with the drawings.

[0028] Reference figure 1 , A link prediction method based on local community information, including the following steps:

[0029] Step 1: Establish a network model G(V, E), where V is a node in the network, and E is an edge between nodes in the network, and calculate the co-match coefficient r of the network:

[0030] r = M - 1 X i j i k i - ( M - 1 X i 1 2 ( j i + k i ) ) 2 M - 1 X i 1 2 ( j i 2 + k i 2 ) - ( M - 1 X i 1 2 ( j i + k i ) ) 2 ,

[0031] Where j i And k i Is the degree value of the two nodes of the i-th edge, i=1, 2, 3...M, and M is the total number of edges;

[0032] Step 2: Take the unconnected node pairs in the network as candidate node pairs, prepare to predict the unknown or fu...

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Abstract

The invention provides a link prediction method based on local community information. The link prediction method comprises the following steps: step 1, establishing a network model G, and calculating an assortativity coefficient of the network; step 2, using node pairs without connecting edges in the network as candidate node pairs, preparing to predict an unknown or a future link among these node pairs, and recording the node degree of two nodes; step 3, calculating DU by referring to both the assortativity coefficient and the node degrees of the node pairs without connecting edges; step 4, extracting and establishing a common neighbor network formed by the current two candidate nodes and a common neighbor node between the two candidate nodes; step 5, extracting and establishing a local community network where the common neighbor node is located, and calculating the similarity of the current two candidate nodes; step 6, establishing a similarity list of node pairs arranged in a similarity descending order; and step 7, obtaining the node pairs at the front of the similarity list to serve as the node pairs which are most likely to generate a connected edge in the future and are obtained by a link prediction algorithm. The link prediction method provided by the invention has relatively high reliability and a good prediction effect.

Description

Technical field [0001] The present invention relates to the field of network science and link prediction, in particular to a link prediction method based on local community information. Background technique [0002] Link prediction is an important branch of network science. It refers to how to predict the possibility of connecting edges between two nodes in a given network that do not yet have edges in a given network through various known information. There are many different perspectives for link prediction in complex networks, and the method of predicting new connections in sparse networks based on the local topology information of the network is a widely used idea. [0003] At present, scholars at home and abroad use the local topology information of the network to study link prediction, hoping to find out some important characteristics of the actual network through this type of research and optimize the recommendation algorithm accordingly. There have been many theoretical re...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/26
Inventor 杨旭华凌非
Owner ZHEJIANG UNIV OF TECH
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