Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (12): 245-251.doi: 10.11896/jsjkx.190700020

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TNTlink Prediction Model Based on Feature Learning

WANG Hui1,2, LE Zi-chun3, GONG Xuan1, ZUO Hao1, WU Yu-kun1   

  1. 1 College of Computer Science and Technology Zhejiang University of Technology Hangzhou 310023,China
    2 College of Applied ScienceJiangxi University of Science and Technology Ganzhou Jiangxi 341000,China
    3 College of Science Zhejiang University of Technology Hangzhou 310023,China
  • Received:2019-07-02 Revised:2019-12-07 Published:2020-12-17
  • About author:WANG Hui,,born in 1983,Ph.D student,lecturer,is a member of China Computer Federation.Her main re-search interests include link prediction,deep learning,AI and big data.
  • Supported by:
    Special Funding of “the Belt and Road” International Cooperation of Zhejiang Province (2015C04005).

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Abstract: In the co-author networklink prediction can predict the missing links in the current network and the new or disbanded links.It is of great significance for mining and analyzing the evolution of the network and remaking the network model to infer whether the two authors will cooperate in the near future according to the observed information in the network.As an important research direction of computer science and physicslink prediction has been studied in depth up to now.Their main research idea is based on the markov chainmachine learning and unsupervised learning.Howevermost of these work use only a single featurenamely the network topology features or attribute features to predictfew will consider these interdisciplinary featuresand papers combined with multidisciplinary on link prediction are fewer.This paper designed and developed the TNTlink model.This model combines the network topology featuresbasic featues and the additional featurescombines physics and computer science domain knowledgeand uses the depth of neural network to integrate these features into a deep learning framework dealing with the problem of link predictionand good results have been achieved.Five data sets (ca-astrophca-condmatca-grqcca-hepph and ca-hepth) were used in this papercontaining 69032 nodes and 450617 edges.Binary similarity and fuzzy cosine similarity were used to calculate and identify these features from captured information.If nodes show more similarity in these features (for examplesimilar nodesthe same keywordsor a close relationship between them)the two nodes are more likely to generate links.Besides the features of nodesthe influence of node importance on link formation was also considered.A new link prediction index MI was proposed to distinguish strong effects from weak effects and to model the important effects of nodes.The proposed model was compared with mainstream classifiers on five datasets.The results show that MI and TNTlink can effectively improve link prediction AUC value.

Key words: Additional features, Basic features, Deep learning, Fuzzy cosine similarity, Link prediction, Topological features

CLC Number: 

  • TP391
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