Multi-level relationship outlier detection

Abstract

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.

Multilayer Social Network Overlapping Community Detection Algorithm Based on Trust Relationship

“…(3) Through the analysis of the community detection results of the synthetic network and the real network, we verify the correctness of the proposed community nature (4) In the synthetic network, we compare and analyze the proposed algorithm with the contrast algorithm (5) In the real network, we compare and analyze the proposed algorithm with the contrast algorithm Throughout the experiment, according to a previous literature, we set the parameter σ = 1:866. β reflects the degree of community overlap. The higher the value of β, the more nodes shared between communities.…”

“…Therefore, multilayer social networks are proposed to represent the complex network of relationships between people in the real world. In a multilayer social network, the nodes are users, and each layer represents a type of user's social relationship [3][4][5].…”