Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crime Prediction with Graph Neural Networks and Multivariate Normal
  Distributions   Existing approaches to the crime prediction problem are unsuccessful in
expressing the details since they assign the probability values to large
regions. This paper introduces a new architecture with the graph convolutional
networks (GCN) and multivariate Gaussian distributions to perform
high-resolution forecasting that applies to any spatiotemporal data. We tackle
the sparsity problem in high resolution by leveraging the flexible structure of
GCNs and providing a subdivision algorithm. We build our model with Graph
Convolutional Gated Recurrent Units (Graph-ConvGRU) to learn spatial, temporal,
and categorical relations. In each node of the graph, we learn a multivariate
probability distribution from the extracted features of GCNs. We perform
experiments on real-life and synthetic datasets, and our model obtains the best
validation and the best test score among the baseline models with significant
improvements. We show that our model is not only generative but also precise.

15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
20
The categories of this paper are: acc-phys
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org