Network unknown connection edge prediction method based on second-order local community and preferential attachment

A predictive network and local technology, applied in the direction of data exchange network, digital transmission system, electrical components, etc., can solve the problems of low information utilization rate and low accuracy rate, 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 shortcomings of the existing link prediction algorithm, such as low accuracy rate and low information utilization rate, the present invention proposes a link prediction method based on second-order local community sum and preference connection with high accuracy rate and good prediction effect

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  • Network unknown connection edge prediction method based on second-order local community and preferential attachment
  • Network unknown connection edge prediction method based on second-order local community and preferential attachment
  • Network unknown connection edge prediction method based on second-order local community and preferential attachment

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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 preferred connections, 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 in , 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...

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Abstract

The invention provides a network unknown connection edge prediction method based on a second-order local community and preferential attachment. The method comprises the following steps: constructing a network model, and obtaining first-order common neighbor nodes and second-order common neighbor nodes of a pair of unconnected nodes, wherein the nodes and the connection edges between the nodes form the second-order local community; recording the total number of the nodes and connection edges of the local community, and meanwhile, recording the number of neighbors, outside the community, of two seed nodes; calculating volume coefficient, edge-clustering coefficient and simple harmonic average distance of the community and second-order local community coefficient; calculating similarity score index between the node pair; traversing the whole network, and for any two unconnected nodes, calculating similarity score index between the corresponding node pair; and ranking similarity scores between all of the unconnected node pairs in a descending order, and taking nodes corresponding to the front m score indexes as predicted connection edges. The method takes the second-order local community and preferential attachment information of the seed nodes into consideration, and makes full utilization of information of a network local structure, and is good in prediction effect and high in accuracy.

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 preferred connections. 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 incompletene...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L12/24
CPCH04L41/12H04L41/142H04L41/145H04L41/147
Inventor 杨旭华程之金林波
Owner ZHEJIANG UNIV OF TECH
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