Journal of Jilin University(Engineering and Technology Edition) ›› 2018, Vol. 48 ›› Issue (5): 1571-1577.doi: 10.13229/j.cnki.jdxbgxb20170717

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Logistic regression classification in networked data with heterophily based on second-order Markov assumption

DONG Sa1,2, LIU Da-you1,2, OUYANG Ruo-chuan3, ZHU Yun-gang1,2, LI Li-na1,2   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China;
    3.Big Data and Network Management Center, Jilin University, Changchun 130012, China
  • Received:2017-07-10 Online:2018-09-20 Published:2018-12-11

Abstract: A logistic regression classifier based on second-order Markov assumption is proposed for heterophilous network. The algorithm employs the second-order Markov links, extends neighbors' link distributions of node neighbors in order to construct structured logistic regression model. At the same time it combines the first-order Markov logistic regression model to update the class distributions progressively using relaxation labeling collective inference method. Comparison of experiment results shows that the enhanced algorithm performs better on the heterophilious network.

Key words: artificial intelligence, heterophilous network classification, structured logistic regression model, networked data

CLC Number: 

  • TP301
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