Co-ranking authors and documents in a heterogeneous network

Ding Zhou; Sergey A. Orshanskiy; Hongyuan Zha; C. Lee Giles

doi:10.1109/ICDM.2007.57

Co-ranking authors and documents in a heterogeneous network

Ding Zhou, Sergey A. Orshanskiy, Hongyuan Zha, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

197 Scopus citations

Abstract

Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.

Original language	English (US)
Title of host publication	Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007
Pages	739-744
Number of pages	6
DOIs	https://doi.org/10.1109/ICDM.2007.57
State	Published - 2007
Event	7th IEEE International Conference on Data Mining, ICDM 2007 - Omaha, NE, United States Duration: Oct 28 2007 → Oct 31 2007

Publication series

Name	Proceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)	1550-4786

Other

Other	7th IEEE International Conference on Data Mining, ICDM 2007
Country/Territory	United States
City	Omaha, NE
Period	10/28/07 → 10/31/07

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/ICDM.2007.57

Cite this

@inproceedings{db3de297c840422183758f1e5085d664,

title = "Co-ranking authors and documents in a heterogeneous network",

abstract = "Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.",

author = "Ding Zhou and Orshanskiy, {Sergey A.} and Hongyuan Zha and Giles, {C. Lee}",

year = "2007",

doi = "10.1109/ICDM.2007.57",

language = "English (US)",

isbn = "0769530184",

series = "Proceedings - IEEE International Conference on Data Mining, ICDM",

pages = "739--744",

booktitle = "Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007",

note = "7th IEEE International Conference on Data Mining, ICDM 2007 ; Conference date: 28-10-2007 Through 31-10-2007",

}

Zhou, D, Orshanskiy, SA, Zha, H & Giles, CL 2007, Co-ranking authors and documents in a heterogeneous network. in Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007., 4470320, Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 739-744, 7th IEEE International Conference on Data Mining, ICDM 2007, Omaha, NE, United States, 10/28/07. https://doi.org/10.1109/ICDM.2007.57

Co-ranking authors and documents in a heterogeneous network. / Zhou, Ding; Orshanskiy, Sergey A.; Zha, Hongyuan et al.
Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007. 2007. p. 739-744 4470320 (Proceedings - IEEE International Conference on Data Mining, ICDM).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Co-ranking authors and documents in a heterogeneous network

AU - Zhou, Ding

AU - Orshanskiy, Sergey A.

AU - Zha, Hongyuan

AU - Giles, C. Lee

PY - 2007

Y1 - 2007

N2 - Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.

AB - Recent graph-theoretic approaches have demonstrated remarkable successes for ranking networked entities, but most of their applications are limited to homogeneous networks such as the network of citations between publications. This paper proposes a novel method for co-ranking authors and their publications using several networks: the social network connecting the authors, the citation network connecting the publications, as well as the authorship network that ties the previous two together. The new co-ranking framework is based on coupling two random walks, that separately rank authors and documents following the PageRank paradigm. As a result, improved rankings of documents and their authors depend on each other in a mutually reinforcing way, thus taking advantage of the additional information implicit in the heterogeneous network of authors and documents.

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DO - 10.1109/ICDM.2007.57

M3 - Conference contribution

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SN - 9780769530185

T3 - Proceedings - IEEE International Conference on Data Mining, ICDM

SP - 739

EP - 744

BT - Proceedings of the 7th IEEE International Conference on Data Mining, ICDM 2007

T2 - 7th IEEE International Conference on Data Mining, ICDM 2007

Y2 - 28 October 2007 through 31 October 2007

ER -

Co-ranking authors and documents in a heterogeneous network

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