Multi-level relationship outlier detection

Relationship management is critical in business. Particularly, it is important to detect abnormal relationships, such as fraudulent relationships between service providers and consumers. Surprisingly, in the literature there is no systematic study on detecting relationship outliers. Particularly, no existing methods can detect and handle relationship outliers between groups and individuals in groups. In this thesis, we tackle this important problem by developing a simple yet effective model. We identify two types of outliers and devise efficient detection algorithms. Our experiments on both real data sets and synthetic ones confirm the effectiveness and efficiency of our approach.