HetPathMine: A Novel Transductive Classification Algorithm on Heterogeneous Information Networks

Chen Luo, Renchu Guan, Zhe Wang, Chenghua Lin

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

38 Citations (Scopus)


Transductive classification (TC) using a small labeled data to help classifying all the unlabeled data in information networks. It is an important data mining task on information networks. Various classification methods have been proposed for this task. However, most of these methods are proposed for homogeneous networks but not for heterogeneous ones, which include multi-typed objects and relations and may contain more useful semantic information. In this paper, we firstly use the concept of meta path to represent the different relation paths in heterogeneous networks and propose a novel meta path selection model. Then we extend the transductive classification problem to heterogeneous information networks and propose a novel algorithm, named HetPathMine. The experimental results show that: (1) HetPathMine can get higher accuracy than the existing transductive classification methods and (2) the weight obtained by HetPathMine for each meta path is consistent with human intuition or real-world situations.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publication36th European Conference on IR Research, ECIR 2014, Amsterdam, The Netherlands, April 13-16, 2014. Proceedings
EditorsMaarten de Rijke, Tom Kenter, Arjen P. de Vries, ChengXiang Zhai, Franciska de Jong, Kira Radinsky, Katja Hofman
Number of pages12
ISBN (Electronic)978-3-319-06028-6
ISBN (Print)978-3-319-06027-9
Publication statusPublished - 2014

Publication series

NameLecture Notes in Computer Science
Volume 8416
ISSN (Electronic)0302-9743


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