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Research On Link Prediction Based On Network Local Structure

Posted on:2019-03-12Degree:MasterType:Thesis
Country:ChinaCandidate:X F WangFull Text:PDF
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In recent years,more and more attention has been paid to the link prediction problem in the network.It is a meaningful research direction to predict people's relationship and behavior in real life through link prediction in the virtual world.Researchers from different angles design different indicators to explore continuously.Because there are many characteristics of the relationship between network nodes,the difference of feature selection will affect the prediction effect of similarity index,so the measurement index of similarity has a great development space.In this paper,we have done further work on the basis of two types of networks,simple undirected networks and symbolic networks,and the results are as follows:(1)In a simple and undirected network,because the common neighbors are at the core of the nodes to be predicted,its influence is obvious.This article is specifically considered from two aspects.On the one hand,we describe the phenomenon from the connectivity of the common neighbors in the network.In this paper,we take the ratio of the number of common neighbor nodes of two nodes to the number of neighbor nodes of the two nodes.The larger the ratio is,the more information is transmitted.On the other hand,the resource allocation from one node to another is analyzed.This paper assumes that the allocation is not uniform.It is considered that the larger the number of common neighbors of the two nodes,the more resources will be demanded from the common neighbors.Combined with these two ideas,the new index of this article is put forward: BCNI index.The experimental verification is carried out with the AUC evaluation index.The results of actual data analysis show that the method improves the similarity index of local information based on nodes effectively.(2)Symbolic network,that is,there are many different types of edges in the network.The most typical is the positive side and the negative side.How to predict the positive edges and the negative edges has gradually become an increasingly important research topic.Understand that in the social network of local path index(LP)of good performance,and structural balance theory as the theoretical basis for the study of symbolic network has certain practical significance.In this paper,LP algorithm and structural balance theory are applied to the symbolic network.The AUC evaluation index is used to verify the experiment.LP index is also suitable for symbol network,and its balance theory explanation is given.Whether it is a simple undirected network or a symbolic network,the proposed method is applied to real networks and partly selected real networks,which is effective and feasible after experimental verification,and has certain application value.
Keywords/Search Tags:complex network, symbolic network, link prediction, common neighbor, similarity index
PDF Full Text Request
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