Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Suspicious Massive Registration Detection via Dynamic Heterogeneous
  Graph Neural Networks   Massive account registration has raised concerns on risk management in
e-commerce companies, especially when registration increases rapidly within a
short time frame. To monitor these registrations constantly and minimize the
potential loss they might incur, detecting massive registration and predicting
their riskiness are necessary. In this paper, we propose a Dynamic
Heterogeneous Graph Neural Network framework to capture suspicious massive
registrations (DHGReg). We first construct a dynamic heterogeneous graph from
the registration data, which is composed of a structural subgraph and a
temporal subgraph. Then, we design an efficient architecture to predict
suspicious/benign accounts. Our proposed model outperforms the baseline models
and is computationally efficient in processing a dynamic heterogeneous graph
constructed from a real-world dataset. In practice, the DHGReg framework would
benefit the detection of suspicious registration behaviors at an early stage.

22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
18
The categories of this paper are: econ
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
3
The categories of this paper are: physics
28
The categories of this paper are: alg-geom