Network unknown edge prediction method based on second-order local community and node degree information

A technology for predicting network and node degrees, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of low information utilization and low accuracy, and achieve the effect of improving accuracy and high precision

Inactive Publication Date: 2017-01-11
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the problems of low accuracy and low information utilization of existing link prediction algorithms, the present invention proposes a link prediction method based on second-order local community and node degree information with high accuracy and good prediction effect

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  • Network unknown edge prediction method based on second-order local community and node degree information
  • Network unknown edge prediction method based on second-order local community and node degree information
  • Network unknown edge prediction method based on second-order local community and node degree information

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Embodiment Construction

[0024] The present invention will be further described below in conjunction with the accompanying drawings.

[0025] refer to figure 1 , a method for predicting unknown connections in a network based on second-order local communities and node degree information, including the following steps:

[0026] Step 1: Establish a network model G(V,E) under the condition that the entire network remains connected, where V is a node in the network, and E is an edge in the network;

[0027] Step 2: Select a pair of nodes i and j without edges in the network as two seed nodes, namely figure 1 In the middle black circle, extract all the first-order common neighbor nodes and second-order common neighbor nodes of i and j and the edges between these nodes, such as figure 1 The white dots and their connected edges constitute a second-order local community, where a node in the middle of a path of length 2 between i and j is a first-order common neighbor, and two nodes in the middle of a path of...

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Abstract

The invention provides a network unknown edge prediction method based on a second-order local community and node degree information. A network model is constructed, the first-order common neighbor nodes and the second-order common neighbor nodes of a pair of unconnected nodes are acquired, and the nodes and the edges between the nodes form the second-order local community; the total number of the nodes and the edges of the community is recorded, and the degree of each node in the whole network and the degree in the community are also recorded; the degree coefficient, the edge-clustering coefficient, the harmonic average distance and the second-order local community coefficient of the community are calculated; the similarity score index between the node pairs is calculated; and the whole network is traversed, the similarity score index between the corresponding node pairs is calculated as for any two unconnected nodes, the similarity scores of all the unconnected node pairs are ordered in a descending way, and the corresponding node pairs of the previous m indexes are taken to act as the prediction edges. The second-order local community and the node degree information are considered, and network local structure information is fully utilized so that the prediction effect is great and the accuracy is high.

Description

technical field [0001] The invention relates to the field of network and link prediction, in particular to a method for predicting unknown network edges based on second-order local communities and node degree information. Background technique [0002] With the rapid development of science, human beings have entered the network age. Various technologies and industries based on the Internet have emerged as the times require, greatly improving people's learning and life. We live in all kinds of networks. When you interact with people, you will have a network of relationships, and when you travel, you will have a transportation network. With the rapid development of natural science, we know more and more about the world. The network of human research is becoming larger and more complex. In the context of today's big data, as the size of individual data and the total size of data that need to be processed increase, the average quality of data is declining, while the incomplete...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/00
CPCG06Q10/04G06Q50/01
Inventor 杨旭华程之
Owner ZHEJIANG UNIV OF TECH
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