Detecting Outlier in Graph Structure Data Using Centrality

نویسندگان

  • Yukihiro Takayama
  • Ryosuke Saga
  • Takao Miyamoto
چکیده

This study describes an outlier detection technique for graph structure data that uses the centrality index. Existing techniques set thresholds for link and node regularity. However, existing techniques are not objective and do not apply to data without the link strength information. Therefore, we pay attention to centrality, which is an index used in network analysis. We perform outlier detection objectively and on the basis of a fixed rule by using centrality. Using the proposed technique on two kinds of data, results show that unifying two or more centrality indexes for outlier detection is the most useful approach. Keyword: data mining, outlier detection, graph structure data, centrality

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تاریخ انتشار 2013