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Fraud detection on dynamic graphs via temporal path aggregation
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AI
DOI：10.1007/s10489-026-07355-2.png)
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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
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