WEB:a fraud prediction method of Internet lending using network embedding

Cheng WANG; Pengfei SHU

doi:10.11959/j.issn.2096-0271.2019052

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WEB:a fraud prediction method of Internet lending using network embedding

APPLICATION | 更新时间：2024-06-03

- WEB:a fraud prediction method of Internet lending using network embedding
- Big Data Research Vol. 5, Issue 6, Pages: 2019052-1(2019)
- 作者机构：
  
  1. 同济大学计算机科学与技术系，上海 201804
  2. 嵌入式系统与服务计算教育部重点实验室，上海 201804
  3. 上海智能科学与技术研究院，上海 200092
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2019052
  CLC： TP181
- Online First：2019-11，
  
  Published：15 November 2019
- 稿件说明：
移动端阅览
Cheng WANG, Pengfei SHU. WEB:a fraud prediction method of Internet lending using network embedding[J]. Big Data Research, 2019, 5(6): 2019052-1.
DOI：

Cheng WANG, Pengfei SHU. WEB:a fraud prediction method of Internet lending using network embedding[J]. Big Data Research, 2019, 5(6): 2019052-1. DOI： 10.11959/j.issn.2096-0271.2019052.

摘要

基于关联图谱的互联网借贷欺诈预测方法限制了特征的挖掘效率、挖掘深度以及特征的可复用性、可表达性。针对此问题，引入网络嵌入技术，在保留欺诈特征的前提下，将网络中的节点嵌入低维的向量空间，利用向量对网络中的结构和语义信息进行表达；提出了基于周期性时间窗口的网络更新方法和决策批处理方法来提升网络嵌入在精准性和实时性方面的性能。实验表明，网络嵌入技术能够自动有效地学习网络中隐含的关联关系与特征；通过将传统方法和网络嵌入方法相结合，欺诈预测性能得到了显著提升。

Abstract

Internet lending fraud prediction method based on association graph limits the mining efficiency and depth of features

as well as the reusability and expressibility of features.To solve this problem

the network embedding technology was introduced

and the structure and semantic information in the network by using the vector was expressed.The network update method based on periodic time window and decision batch method were proposed to improve the performance of network embedding in the two business requirements of accuracy and real-time.The experiment shows that the network embedding technology can automatically and effectively learn the implicit relationship and characteristics of the network.By combining the traditional method and the network embedding method

the fraud prediction performance has been significantly improved.

关键词

Keywords

references

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