Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Lookup subnet based Spatial Graph Convolutional neural Network   Convolutional Neural Networks(CNNs) has achieved remarkable performance
breakthrough in Euclidean structure data. Recently, aggregation-transformation
based Graph Neural networks(GNNs) gradually produce a powerful performance on
non-Euclidean data. In this paper, we propose a cross-correlation based graph
convolution method allowing to naturally generalize CNNs to non-Euclidean
domains and inherit the excellent natures of CNNs, such as local filters,
parameter sharing, flexible receptive field, etc. Meanwhile, it leverages
dynamically generated convolution kernel and cross-correlation operators to
address the shortcomings of prior methods based on aggregation-transformation
or their approximations. Our method has achieved or matched popular
state-of-the-art results across three established graph benchmarks: the Cora,
Citeseer, and Pubmed citation network datasets.

26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th