Prediction network unknown connection edge method based on second-order local association information

A technology for forecasting networks and communities, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as low accuracy and low information utilization, and achieve high accuracy and improve accuracy.

Inactive Publication Date: 2017-01-04
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, this invention proposes a link prediction method based on second-order local community information with high accuracy and good prediction effect

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  • Prediction network unknown connection edge method based on second-order local association information
  • Prediction network unknown connection edge method based on second-order local association information
  • Prediction network unknown connection edge method based on second-order local association information

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Embodiment Construction

[0023] The present invention will be further described below in conjunction with the accompanying drawings.

[0024] refer to figure 1 , a method for predicting unknown connections in a network based on second-order local community information, including the following steps:

[0025] 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;

[0026] 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 length 3 are two ...

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Abstract

The present invention provides a prediction network unknown connection edge method based on the second-order local association information. The method comprises: constructing a network model; obtaining a pair of first-order common neighbor nodes and second-order common neighbor nodes which are not connected with nodes, wherein the connection edge between the nodes and the first-order common neighbor nodes and the second-order common neighbor nodes forms a second-order local association; recording the total number of the nodes of the association and the total number of the connection edge; calculating the edge clustering coefficient, the simple harmonic quantity mean distance of the association and the second-order association coefficients; calculating the similarity score index between the node pair; and traversing the whole network, calculating the similarity score index between the corresponding node pair aiming at two random non-connected nodes, and sorting the similarity scores among all the non-connected node pairs in the descending order to take the nodes corresponding to the front M indexes as predication connection edge. The prediction network unknown connection edge method based on the second-order local association information considers the second-order local association including the first-order common neighbor and the second-order common neighbor between two non-connected nodes to fully utilize the network local structure information, the predication effect is good, 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 community 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 incompleteness of data The ...

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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 杨旭华俞佳项旗立许齐功
Owner ZHEJIANG UNIV OF TECH
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