Tensor based relations ranking for multi-relational collective classification

Chao Han, Qingyao Wu, Kwok Po NG, Jiezhang Cao, Mingkui Tan, Jian Chen

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

2 Citations (Scopus)

Abstract

In this paper, we study relations ranking and object classification for multi-relational data where objects are interconnected by multiple relations. The relations among objects should be exploited for achieving a good classification. While most existing approaches exploit either by directly counting the number of connections among objects or by learning the weight of each relation from labeled data only. In this paper, we propose an algorithm, TensorRRCC, which is able to determine the ranking of relations and the labels of objects simultaneously. Our basic idea is that highly ranked relations within a class should play more important roles in object classification, and class membership information is important for determining a ranking quality over the relations w.r.t. a specific learning task. TensorRRCC implements the idea by modeling a Markov chain on transition probability graphs from connection and feature information with both labeled and unlabeled objects and propagates the ranking scores of relations and relevant classes of objects. An iterative progress is proposed to solve a set of tensor equations to obtain the stationary distribution of relations and objects. We compared our algorithm with current collective classification algorithms on two real-world data sets and the experimental results show the superiority of our method.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Data Mining, ICDM 2017
EditorsGeorge Karypis, Srinivas Alu, Vijay Raghavan, Xindong Wu, Lucio Miele
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages901-906
Number of pages6
ISBN (Electronic)9781538638347
DOIs
Publication statusPublished - 15 Dec 2017
Event17th IEEE International Conference on Data Mining, ICDM 2017 - New Orleans, United States
Duration: 18 Nov 201721 Nov 2017

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2017-November
ISSN (Print)1550-4786

Conference

Conference17th IEEE International Conference on Data Mining, ICDM 2017
Country/TerritoryUnited States
CityNew Orleans
Period18/11/1721/11/17

Scopus Subject Areas

  • Engineering(all)

User-Defined Keywords

  • Classification
  • Multi-relational data
  • Relations ranking
  • Tensor

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