Temporal Insights for Group-Based Fraud Detection on e-Commerce Platforms

Oct 31, 2024·
Jianke Yu
Jianke Yu
,
Hanchen Wang
,
Xiaoyang Wang
,
Zhao Li
,
Lu Qin
,
Wenjie Zhang
,
Jian Liao
,
Ying Zhang
,
Bailin Yang
· 1 min read
CCF-A CAS Zone 1 Top JCR Q1
Abstract
Along with the rapid technological and commercial innovation on e-commerce platforms, an increasing number of frauds cause great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, no existing research can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds. Based on this, we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. In addition, we introduce a module namedTemporal Group Dynamics Analyzer(TGDA) that strengthens the ability to analyze temporal information on group fraudulent activity. Based on this, we built an enhanced model named TGFDN. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show our proposed model’s superior effectiveness and efficiency for group-based fraud detection on bipartite graphs.
Type
Publication
IEEE Transactions on Knowledge and Data Engineering
Status
Peer-reviewed
Awards
CCF-A
IEEE Transactions on Knowledge and Data Engineering · 2024
CAS Zone 1 Top
IEEE Transactions on Knowledge and Data Engineering · 2024
JCR Q1
IEEE Transactions on Knowledge and Data Engineering · 2024
publications

IEEE Transactions on Knowledge and Data Engineering, 2024

Along with the rapid technological and commercial innovation on e-commerce platforms, an increasing number of frauds cause great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, no existing research can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds. Based on this, we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. In addition, we introduce a module namedTemporal Group Dynamics Analyzer(TGDA) that strengthens the ability to analyze temporal information on group fraudulent activity. Based on this, we built an enhanced model named TGFDN. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show our proposed model’s superior effectiveness and efficiency for group-based fraud detection on bipartite graphs.

Jianke Yu
Authors
Jianke Yu (he/him)
PhD (2026) in Graph Machine Learning & Databases
I completed my PhD at the University of Technology Sydney (UTS) in 2026, supervised by Prof. Ying Zhang, A/Prof. Lu Qin and Dr Hanchen Wang. My research develops machine learning algorithms for graph-structured data and database systems — graph neural network architectures, and learning-based methods that improve database and data-mining algorithms (fraud detection, graph similarity, supergraph search, malware detection). My work has appeared in KDD and IEEE TKDE. I am joining Zhejiang Gongshang University as a faculty member in 2027.