University of Illinois Urbana-Champaign

Graph regularized transductive classification on heterogeneous information networks

Ji, Ming

Content Files
Ji_Ming.pdf

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https://hdl.handle.net/2142/29721

Description

Title
Graph regularized transductive classification on heterogeneous information networks
Author(s)
Ji, Ming
Issue Date
2012-02-06T20:12:51Z
Director of Research (if dissertation) or Advisor (if thesis)
Han, Jiawei
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2012-02-06T20:12:51Z
Keyword(s)
  • classification
  • heterogeneous information networks
Abstract
A heterogeneous information network is a network composed of multiple types of objects and links. Recently, it has been recognized that strongly-typed heterogeneous information networks are prevalent in the real world. Sometimes, label information is available for some objects. Learning from such labeled and unlabeled data via transductive classification can lead to good knowledge extraction of the hidden network structure. However, although classification on homogeneous networks has been studied for decades, classification on heterogeneous networks has not been explored until recently. In this paper, we consider the transductive classification problem on heterogeneous networked data which share a common topic. Only some objects in the given network are labeled, and we aim to predict labels for all types of the remaining objects. A novel graph-based regularization framework, GNetMine, is proposed to model the link structure in information networks with arbitrary network schema and arbitrary number of object/link types. Specifically, we explicitly respect the type differences by preserving consistency over each relation graph corresponding to each type of links separately. Efficient computational schemes are then introduced to solve the corresponding optimization problem. Experiments on the DBLP data set show that our algorithm significantly improves the classification accuracy over existing state-of-theart methods.
Graduation Semester
2011-12
Permalink
http://hdl.handle.net/2142/29721
Copyright and License Information
Copyright 2011 Ming Ji

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