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<record>
  <title>Comparing Network Centrality Measures as Tools for Identifying Key Concepts in Complex Networks: A Case of Wikipedia</title>
  <journal>Journal of Digital Information Management</journal>
  <author>Neven Matas, Sanda Martincic-IpÅ¡ic, Ana MeÅ¡trovic</author>
  <volume>15</volume>
  <issue>4</issue>
  <year>2017</year>
  <doi></doi>
  <url>http://dline.info/fpaper/jdim/v15i4/jdimv15i4_3.pdf</url>
  <abstract>Network centralities are amongst the most
important measures for tracking and locating crucial nodes
in a network. In this paper, we propose a general approach
for identifying the most suitable centrality measure for
detecting key concepts in a semantic or linguistic network.
We experiment with seven network centrality measures
(degree centrality, betweenness centrality, closeness
centrality, eigenvector centrality, current-flow betweenness
centrality, current-flow closeness centrality and
communicability centrality). For the purpose of evaluation,
we compare the original Wikipedia hyperlink network with
a constructed concept network. The obtained results
indicate that all seven used measures have good potential
for identifying key terms, and that degree centrality
achieves the best score. A good score is also obtained
for current-flow betweenness centrality and current-flow
closeness centrality.</abstract>
</record>
