Name
Abstract | ||
|---|---|---|
Fraud detection is an increasingly important and difficult task in the modern technological environment. Existing classification methods for fraud detection are based on the call behavior attributes, such as duration and frequency of callings. With the evolution of the fraud mode, the methods based on self-attributes cannot detect the fraudsters who camouflage themselves by behaving like normal accounts. So we propose an Attention-based Graph Representation learning Model(AGRM), which takes into account the node's self-attributes and its context information. In our model, an attention architecture is introduced to learn the discriminative representation by focusing on the most informative context. On this basis, we design a self-attention mechanism to adjust the contribution of the node's self-attributes and its neighbors'. Extensive experiments show that the proposed method achieved significant accuracy improvement compared with existing fraud detection methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICC.2019.8761665 | IEEE International Conference on Communications |
Keywords | Field | DocType |
fraud detection,graph representation learning,attention mechanism | Architecture,Computer science,Real-time computing,Camouflage,Artificial intelligence,Discriminative model,Machine learning,Graph (abstract data type) | Conference |
ISSN | Citations | PageRank |
1550-3607 | 1 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming Liu | 1 | 276 | 50.00 |
| Jianxin Liao | 2 | 457 | 82.08 |
| J. Wang | 3 | 479 | 95.23 |
| Qi Qi | 4 | 210 | 56.01 |