Name
Abstract | ||
|---|---|---|
•Analyses of nine centrality measures with Structural Holes used for the first time in keyword extraction.•Centrality measures are correlated and with statistical similar performance when finding keywords.•Proposal of the multi-centrality index (MCI) to combine the most representative measures.•MCI achieves a high precision, recall, and F1-score with statistical significance.•Clustering algorithms could not identify well the keyword group as the MCI approach. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ipm.2019.102063 | Information Processing & Management |
Keywords | Field | DocType |
Automatic keyword extraction,Centrality measures,Complex networks,Network science,Text mining,Text networks,Clustering | PageRank,Data mining,Structural holes,Computer science,Keyword extraction,Closeness,Centrality,Betweenness centrality,Cluster analysis,Clustering coefficient | Journal |
Volume | Issue | ISSN |
56 | 6 | 0306-4573 |
Citations | PageRank | References |
3 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Didier A. Vega-Oliveros | 1 | 24 | 5.37 |
| Pedro Spoljaric Gomes | 2 | 3 | 0.40 |
| Evangelos Milios | 3 | 3073 | 360.46 |
| Lilian Berton | 4 | 16 | 7.82 |