Relevance Search via Bipolar Label Diffusion on Bipartite Graphs

1
Dr. Zhang Liang
Dr. Zhang Liang
2
Ren Lixiao
Ren Lixiao
1 Tianjin University of Science& Technology

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GJCST Volume 12 Issue D10

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The task of relevance search is to find relevant items to some given queries, which can be viewed either as an information retrieval problem or as a semi-supervised learning problem. In order to combine both of their advantages, we develop a new relevance search method using label diffusion on bipartite graphs. And we propose a heat diffusion-based algorithm, namely bipartite label diffusion (BLD). Our method yields encouraging experimental results on a number of relevance search problems.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Dr. Zhang Liang. 2012. \u201cRelevance Search via Bipolar Label Diffusion on Bipartite Graphs\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 12 (GJCST Volume 12 Issue D10): .

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GJCST Volume 12 Issue D10
Pg. 33- 38
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Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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v1.2

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August 22, 2012

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English

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The task of relevance search is to find relevant items to some given queries, which can be viewed either as an information retrieval problem or as a semi-supervised learning problem. In order to combine both of their advantages, we develop a new relevance search method using label diffusion on bipartite graphs. And we propose a heat diffusion-based algorithm, namely bipartite label diffusion (BLD). Our method yields encouraging experimental results on a number of relevance search problems.

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Relevance Search via Bipolar Label Diffusion on Bipartite Graphs

Dr. Zhang Liang
Dr. Zhang Liang Tianjin University of Science& Technology
Ren Lixiao
Ren Lixiao

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