吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 522-527.doi: 10.13229/j.cnki.jdxbgxb201602029

• Orginal Article • Previous Articles     Next Articles

Relational neighbor algorithm based on class propagation distributions for classification in networked data with heterophily

DONG Sa1, LIU Da-you1, LI Li-na1, OUYANG Ruo-chuan2, CHAI Xiao-li3   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.FAW Co., Ltd., Research and Development Center, Changchun 130011, China;
    3.Computer Office, Aviation University of Air Force, Changchun 130022, China
  • Received:2014-11-24 Online:2016-02-20 Published:2016-02-20

Abstract: The performance of the relational classifiers based on homophily is poor for the classification in networked data with heterophily. To solve this problem, a relational neighbor algorithm based on propagation distributions is proposed for heterophilous networks. The algorithm adopts the second-order Markov assumption and considers the influence from the neighbors of the unlabeled nodes' neighbors. At the same time the algorithm propagates the influence between nodes through computing propagating class vector and propagating reference vector; and by means of combining the relaxation labeling collective inference method, the algorithm continuously updates the results until the class distributions converge. Experiment results show that the proposed algorithm performs better on the networks with heterophily.

Key words: artificial Intelligence, heterophilous network classification, class propagating distribution relational neighbor classifier, collective inference

CLC Number: 

  • TP301
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