Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/24446

Title: Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
Authors: Wu, Jibing
Meng, Qinggang
Deng, Su
Huang, Hongbin
Wu, Yahui
Badii, Atta
Issue Date: 2017
Publisher: © Wu et al. Published by the Public Library of Science.
Citation: WU, J. ... et al, 2017. Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks. PLoS ONE, 12 (2), e0172323.
Abstract: Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic.
Description: This is an Open Access Article. It is published by the Public Library of Science under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/
Sponsor: The work reported in this paper is supported by National Science Foundation of China, No.61401482. Online: http://www.nsfc.gov.cn/. YW received the funding. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Version: Published
DOI: 10.1371/journal.pone.0172323
URI: https://dspace.lboro.ac.uk/2134/24446
Publisher Link: http://dx.doi.org/10.1371/journal.pone.0172323
ISSN: 1932-6203
Appears in Collections:Published Articles (Computer Science)

Files associated with this item:

File Description SizeFormat
journal.pone.0172323.pdfPublished version4.49 MBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.