Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
- URL: http://arxiv.org/abs/2310.04171v3
- Date: Wed, 3 Jan 2024 07:32:11 GMT
- Title: Dynamic Relation-Attentive Graph Neural Networks for Fraud Detection
- Authors: Heehyeon Kim, Jinhyeok Choi, Joyce Jiyoung Whang
- Abstract summary: Graph-based fraud detection methods consider this task as a classification problem with two classes: frauds or normal.
We address this problem using Graph Neural Networks (GNNs) by proposing a dynamic relation-attentive aggregation mechanism.
Experimental results show that our method, DRAG, outperforms state-of-the-art fraud detection methods on real-world benchmark datasets.
- Score: 5.294604210205507
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fraud detection aims to discover fraudsters deceiving other users by, for
example, leaving fake reviews or making abnormal transactions. Graph-based
fraud detection methods consider this task as a classification problem with two
classes: frauds or normal. We address this problem using Graph Neural Networks
(GNNs) by proposing a dynamic relation-attentive aggregation mechanism. Based
on the observation that many real-world graphs include different types of
relations, we propose to learn a node representation per relation and aggregate
the node representations using a learnable attention function that assigns a
different attention coefficient to each relation. Furthermore, we combine the
node representations from different layers to consider both the local and
global structures of a target node, which is beneficial to improving the
performance of fraud detection on graphs with heterophily. By employing dynamic
graph attention in all the aggregation processes, our method adaptively
computes the attention coefficients for each node. Experimental results show
that our method, DRAG, outperforms state-of-the-art fraud detection methods on
real-world benchmark datasets.
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