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Finding topic-level experts in scholarly networks

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Abstract

Expert finding is of vital importance for exploring scientific collaborations to increase productivity by sharing and transferring knowledge within and across different research areas. Expert finding methods, including content-based methods, link structure-based methods, and a combination of content-based and link structure-based methods, have been studied in recent years. However, most state-of-the-art expert finding approaches have usually studied candidates’ personal information (e.g. topic relevance and citation counts) and network information (e.g. citation relationship) separately, causing some potential experts to be ignored. In this paper, we propose a topical and weighted factor graph model that simultaneously combines all the possible information in a unified way. In addition, we also design the Loopy Max-Product algorithm and related message-passing schedules to perform approximate inference on our cycle-containing factor graph model. Information Retrieval is chosen as the test field to identify representative authors for different topics within this area. Finally, we compare our approach with three baseline methods in terms of topic sensitivity, coverage rate of SIGIR PC (e.g. Program Committees or Program Chairs) members, and Normalized Discounted Cumulated Gain scores for different rankings on each topic. The experimental results demonstrate that our factor graph-based model can definitely enhance the expert-finding performance.

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Acknowledgments

This work was supported by the following Grants: (i) Grant No. 20090094110015 from Research Fund for the Doctoral Program of Higher Education of China, (ii) Grant No. BK2008354 and No. BK2010520 from the Natural Science Foundation of Jiangsu Province of China, (iii) Grant No. 2008135 from the “Six Talent Peaks Program” of Jiangsu Province of China.

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Authors and Affiliations

  1. College of Computer and Information, Hohai University, No. 8 Focheng West Road, Jiangning District, Nanjing, 211100, Jiangsu Province, China

    Lili Lin & Zhuoming Xu

  2. School of Library and Information Science, Indiana University, 1320 East 10th Street, LI 025, Bloomington, IN, 47405, USA

    Ying Ding

  3. School of Library and Information Science, Indiana University, 1320 East 10th Street, LI 029, Bloomington, IN, 47405, USA

    Xiaozhong Liu

Authors
  1. Lili Lin
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  2. Zhuoming Xu
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  3. Ying Ding
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  4. Xiaozhong Liu
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Correspondence to Lili Lin.

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Table A1

Comparison of existing expert finding approaches (DOCX 23 kb)

Table A2

Top 10 words associated with each topic (DOCX 19 kb)

Table A3

Top 10 authors for 5 different topics based on our approach and baseline methods (DOCX 25 kb)

Fig. A1

Loopy Max-Product algorithm with serial schedule using random sequences (DOCX 21 kb)

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Lin, L., Xu, Z., Ding, Y. et al. Finding topic-level experts in scholarly networks. Scientometrics 97, 797–819 (2013). https://doi.org/10.1007/s11192-013-0988-6

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