Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Graph Neural Network Training: The Case for Sampling   Graph Neural Networks (GNNs) are a new and increasingly popular family of
deep neural network architectures to perform learning on graphs. Training them
efficiently is challenging due to the irregular nature of graph data. The
problem becomes even more challenging when scaling to large graphs that exceed
the capacity of single devices. Standard approaches to distributed DNN
training, such as data and model parallelism, do not directly apply to GNNs.
Instead, two different approaches have emerged in the literature: whole-graph
and sample-based training.
  In this paper, we review and compare the two approaches. Scalability is
challenging with both approaches, but we make a case that research should focus
on sample-based training since it is a more promising approach. Finally, we
review recent systems supporting sample-based training.

7
The categories of this paper are: hep-th
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph