Outlier detection in network data using the Betweenness Centrality

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 Betweenness Centrality of a vertex in a graph is a measure for the participation of the vertex in the shortest paths in the graph. The Betweenness centrality is widely used in network analyses. Especially in a social network, the recursive computation of the betweenness centralities 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 in finding outlier in social network analyses. Furthermore we show the effectiveness of the new methods using the experiments data.