Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3233/KES-2012-0230 | KES Journal |
Keywords | Field | DocType |
research community,initial seed,maximum flow algorithm,simple method,extracting research community,large bibliographic data,bibliographic data,improved algorithm,research community extraction algorithm,web community,community node | Graph algorithms,Information retrieval,Extraction algorithm,Computer science,Directed graph,Information extraction,Maximum flow problem,Artificial intelligence,Initial Seed,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 1 | 1327-2314 |
Citations | PageRank | References |
0 | 0.34 | 18 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yushi Nakamura | 1 | 1 | 0.69 |
| Toshihiko Horiike | 2 | 3 | 1.06 |
| Tetsuji Kuboyama | 3 | 140 | 29.36 |
| Hiroshi Sakamoto | 4 | 47 | 6.63 |