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Second-order local community and common neighbor proportion information-based method for predicting unknown connected edges of network

A forecasting network, local technology, applied in forecasting, instrumentation, data processing applications, etc., can solve the problems of low accuracy and low information utilization, and achieve the effect of high accuracy and improved accuracy

Inactive Publication Date: 2017-02-01
ZHEJIANG UNIV OF TECH
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Problems solved by technology

[0004] In order to overcome the shortcomings of the existing link prediction algorithm with low accuracy and low information utilization, the present invention proposes a method for predicting unknown network edges based on second-order local communities and common neighbor ratio information with high accuracy and good prediction effect

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  • Second-order local community and common neighbor proportion information-based method for predicting unknown connected edges of network

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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 network edges based on second-order local communities and common neighbor ratio 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 The black dots in the center, the total number of first-order and second-order neighbors of nodes i and j are denoted as T i and T j , where the distance to node i or j is the node whose path length is equal to 1 is the first-order neighbor of node i or j, and the node whose distance to node i or j is equal to the path length equal to 2 is the second-order neighbor of node i or j; Extract all the fi...

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Abstract

The invention discloses a second-order local community and common neighbor proportion information-based method for predicting unknown connected edges of a network. The method comprises the steps of building a network model, taking any pair of unconnected nodes as seed nodes, and recording total quantities of first-order and second-order neighbors of the seed nodes; obtaining first-order and second-order common neighbor nodes of the seed nodes, wherein the nodes and connected edges among the nodes form a second-order local community; recording total quantities of the nodes and the connected edges of the community; calculating a viscosity coefficient, an edge-clustering coefficient, a simple harmonic average distance and a second-order local community coefficient of the community; calculating a similarity score index between node pairs; and traversing the network, calculating a corresponding similarity score index for any two unconnected nodes, arranging similarity scores among all unconnected nodes according to a descending order, and taking the node pairs corresponding to first m indexes as predicted connected edges. According to the method, the second-order local community and the proportion of the common neighbors of the seed nodes in the neighbors are considered, and local structure information of the network is fully utilized, so that the prediction 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 communities and common neighbor ratio 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 ...

Claims

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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 杨旭华程之肖杰
Owner ZHEJIANG UNIV OF TECH
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