Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visualizing Graph Neural Networks with CorGIE: Corresponding a Graph to
  Its Embedding   Graph neural networks (GNNs) are a class of powerful machine learning tools
that model node relations for making predictions of nodes or links. GNN
developers rely on quantitative metrics of the predictions to evaluate a GNN,
but similar to many other neural networks, it is difficult for them to
understand if the GNN truly learns characteristics of a graph as expected. We
propose an approach to corresponding an input graph to its node embedding (aka
latent space), a common component of GNNs that is later used for prediction. We
abstract the data and tasks, and develop an interactive multi-view interface
called CorGIE to instantiate the abstraction. As the key function in CorGIE, we
propose the K-hop graph layout to show topological neighbors in hops and their
clustering structure. To evaluate the functionality and usability of CorGIE, we
present how to use CorGIE in two usage scenarios, and conduct a case study with
five GNN experts.

8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg