Outlier detection in network data using the Betweenness Centrality, 2015

Bosthanthirige, Mihiri Hewa

2010-2019

Outlier detection has been used to detect and, where appropriate, remove anomalous observations from data. It has important applications in the field of fraud detection, network robustness analysis, and intrusion detection. In this paper, we propose a Betweenness Centrality (BEC) as novel to determine the outlier in network analyses. The BEC of a vertex on a graph is a measure for the participation of the vertex in the shortest paths in the graph. The BEC is widely used in network analyses. Especially in a social network, the recursive computation of the BEC of vertices is performed for the community detection and finding the influential user in the network. In this paper, we propose that this method is efficient for finding outlier in social network analyses. Furthermore we show the effectiveness of the new methods.

text

application/pdf

2015-07-01

thesis

Master of Science (MS)

Clark Atlanta University

Computer and Information Sciences

George, Roy

Clark Atlanta University

Georgia--Atlanta

http://hdl.handle.net/20.500.12322/cau.td:2015_bosthanthirige_mihiri_hewa