Date of Award
University or Center
Clark Atlanta University(CAU)
Computer and Information Sciences
Roy George, Ph.D.
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.
Bosthanthirige, Mihiri Hewa, "Outlier detection in network data using the Betweenness Centrality" (2015). ETD Collection for AUC Robert W. Woodruff Library. 3042.