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Cited by 245

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Co-ranking Authors and Documents in a Heterogeneous Network

Zhou, Ding, Orshanskiy, Sergey A., Zha, Hongyuan, Giles, C. Lee·2007·Seventh IEEE International Conference on Data Mining (ICDM 2007)

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 PageRankparadigm. 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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Metadata

Authors
Zhou, D., Orshanskiy, S. A., Zha, H., & Giles, C. L.
Venue
Seventh IEEE International Conference on Data Mining (ICDM 2007)
Publisher
IEEE
Published
2007-10
Pages
739-744
ISSN
1550-4786 (ISSN-L)
OpenAlex
W2162010993
Cited by
245
References
4
FWCI
20.77
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: Co-ranking Authors and Documents in a Heterogeneous Network. https://app.cochat.ai/papers/10.1109/icdm.2007.57
The paper
Zhou, D., Orshanskiy, S. A., Zha, H., & Giles, C. L. (2007). Co-ranking Authors and Documents in a Heterogeneous Network. Seventh IEEE International Conference on Data Mining (ICDM 2007). https://doi.org/10.1109/icdm.2007.57

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.