所属 コンピュータサイエンス学部 コンピュータサイエンス学科 職種 専任講師
|著者・共著者||伏見 卓恭，斉藤 和巳，風間 一洋|
|概要||In this paper, we propose two centrality measures, mixedness and cohesiveness centrality,
intended to extract representative objects in a metric space.
These measures are based on betweenness and closeness centrality
each of which is widely used in the social network analysis field in order to extract important nodes.
The mixedness centrality is an indicator that quantifies the degree to be located medway between any pairs of objects
and extracts mixed objects located in medway of clusters.
By contrast, the cohesiveness centrality is an indicator that quantifies the degree to be located in densely distribution
and extracts cohesive objects located in center of densely distributed objects.
In our experiments using several types of real and synthetic datasets,
we show that our proposed centrality measures can extract characteristic representative objects in each dataset.