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Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

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 ne...

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Autores principales: Wu, Jibing, Meng, Qinggang, Deng, Su, Huang, Hongbin, Wu, Yahui, Badii, Atta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330508/
https://www.ncbi.nlm.nih.gov/pubmed/28245222
http://dx.doi.org/10.1371/journal.pone.0172323
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author Wu, Jibing
Meng, Qinggang
Deng, Su
Huang, Hongbin
Wu, Yahui
Badii, Atta
author_facet Wu, Jibing
Meng, Qinggang
Deng, Su
Huang, Hongbin
Wu, Yahui
Badii, Atta
author_sort Wu, Jibing
collection PubMed
description 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.
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spelling pubmed-53305082017-03-09 Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks Wu, Jibing Meng, Qinggang Deng, Su Huang, Hongbin Wu, Yahui Badii, Atta PLoS One Research Article 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. Public Library of Science 2017-02-28 /pmc/articles/PMC5330508/ /pubmed/28245222 http://dx.doi.org/10.1371/journal.pone.0172323 Text en © 2017 Wu et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Wu, Jibing
Meng, Qinggang
Deng, Su
Huang, Hongbin
Wu, Yahui
Badii, Atta
Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
title Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
title_full Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
title_fullStr Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
title_full_unstemmed Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
title_short Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
title_sort generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5330508/
https://www.ncbi.nlm.nih.gov/pubmed/28245222
http://dx.doi.org/10.1371/journal.pone.0172323
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