Extracting research communities from bibliographic data

Issue title: Intelligent Information Processing: Techniques and Applications

Guest editors: C.P. Limx and L.C. Jainy

Article type: Research Article

Authors: Nakamura, Yushi^a | Horiike, Toshihiko^a | Kuboyama, Tetsuji^b | Sakamoto, Hiroshi^{a; c; *}

Affiliations: [a] Kyushu Institute of Technology, Fukuoka, Japan | [b] Gakushuin University, Tokyo, Japan | [c] JST PRESTO, 4-1-8 Honcho Kawaguchi, Saitama, Japan | [x] University of South Australia, Adelaide, South Australia, Australia | [y] University of Science, Malaysia

Correspondence: [*] Correspondence author: Hiroshi Sakamoto, Ph.D., Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, Fukuoka 820-8502, Japan. Tel.: +81 948 29 7630; Fax: +81 948 29 7611; E-mail: hiroshi@ai.kyutech.ac.jp

Abstract: We develop a research community extraction algorithm from large bibliographic data, which was preliminarily reported in Horiike et al. [10] and Nakamura et al. [18]. A research community in bibliographic data is considered to be a set of the linked texts holding a common topic, in other words, it is a dense subgraph embedded in the directed graph. Our method is based on the maximum flow algorithm for finding web communities by Flake et al. [5]. We propose improvements of the algorithm to select community nodes and initial seeds taking account of the restriction that any directed graph is acyclic. We examine the improved algorithm for the list of keywords frequently appearing in the bibliographic data. In addition we propose a simple method to extract characteristic keywords for deciding initial seed nodes. This method is also evaluated by experiments.

Keywords: Community extraction, graph algorithm, information extraction

DOI: 10.3233/KES-2012-0230

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 16, no. 1, pp. 25-34, 2012