Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Fraud Detection on Non-attributed Graph   Fraud detection problems are usually formulated as a machine learning problem
on a graph. Recently, Graph Neural Networks (GNNs) have shown solid performance
on fraud detection. The successes of most previous methods heavily rely on rich
node features and high-fidelity labels. However, labeled data is scarce in
large-scale industrial problems, especially for fraud detection where new
patterns emerge from time to time. Meanwhile, node features are also limited
due to privacy and other constraints. In this paper, two improvements are
proposed: 1) We design a graph transformation method capturing the structural
information to facilitate GNNs on non-attributed fraud graphs. 2) We propose a
novel graph pre-training strategy to leverage more unlabeled data via
contrastive learning. Experiments on a large-scale industrial dataset
demonstrate the effectiveness of the proposed framework for fraud detection.

10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an