.png){kind=icon}
Return
Fraud detection on dynamic graphs via temporal path aggregation
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-026-07355-2.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Fraud detection in graphs has attracted considerable attention in various domains, where a primary challenge is the data heterophily. Despite the success of existing GNN-based methods on handling heterophily on static graphs, their performance on dynamic graphs are suboptimal due to not fully utilizing two crucial types of temporal information in dynamic graphs: the temporal paths and time spans. To address this problem, we present Temporal Path Aggregator (TPA), a novel dynamic graph fraud detector, which is capable of modeling heterophilic data while capturing the temporal information by sampling and aggregating temporal paths associated with each node. A time span encoder module is also devised to capture the fine-grained time span information. Results of extensive experiments demonstrate that our model outperforms state-of-the-art baselines.
Keywords:
Fraud detection
Graph neural network
Dynamic graph
Deep learning
Journal
IF:
3.5
Papers:
7.6K
Citations:
1.7W
Organization
Cited Papers
SMoTeF: Smurf money laundering detection using temporal order and flow analysis.png)
APPLIED INTELLIGENCE
OAAI
APPLIED INTELLIGENCEIF3.5
OAAIShareSaveCDGAT: a graph attention network method for credit card defaulters predictionAPPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
ShareSave
ShareSaveShareSaveShareSaveCost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors
PREAI
ShareSave no more
no more