Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Classification with 2D Convolutional Neural Networks   Graph learning is currently dominated by graph kernels, which, while
powerful, suffer some significant limitations. Convolutional Neural Networks
(CNNs) offer a very appealing alternative, but processing graphs with CNNs is
not trivial. To address this challenge, many sophisticated extensions of CNNs
have recently been introduced. In this paper, we reverse the problem: rather
than proposing yet another graph CNN model, we introduce a novel way to
represent graphs as multi-channel image-like structures that allows them to be
handled by vanilla 2D CNNs. Experiments reveal that our method is more accurate
than state-of-the-art graph kernels and graph CNNs on 4 out of 6 real-world
datasets (with and without continuous node attributes), and close elsewhere.
Our approach is also preferable to graph kernels in terms of time complexity.
Code and data are publicly available.

25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th