Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Neural Networks in Real-Time Fraud Detection with Lambda
  Architecture   Transaction checkout fraud detection is an essential risk control components
for E-commerce marketplaces. In order to leverage graph networks to decrease
fraud rate efficiently and guarantee the information flow passed through
neighbors only from the past of the checkouts, we first present a novel
Directed Dynamic Snapshot (DDS) linkage design for graph construction and a
Lambda Neural Networks (LNN) architecture for effective inference with Graph
Neural Networks embeddings. Experiments show that our LNN on DDS graph,
outperforms baseline models significantly and is computational efficient for
real-time fraud detection.

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The categories of this paper are: q-alg
33
The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
18
The categories of this paper are: econ
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The categories of this paper are: q-fin
3
The categories of this paper are: physics
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol