Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convolutional Kernel Networks for Graph-Structured Data   We introduce a family of multilayer graph kernels and establish new links
between graph convolutional neural networks and kernel methods. Our approach
generalizes convolutional kernel networks to graph-structured data, by
representing graphs as a sequence of kernel feature maps, where each node
carries information about local graph substructures. On the one hand, the
kernel point of view offers an unsupervised, expressive, and easy-to-regularize
data representation, which is useful when limited samples are available. On the
other hand, our model can also be trained end-to-end on large-scale data,
leading to new types of graph convolutional neural networks. We show that our
method achieves competitive performance on several graph classification
benchmarks, while offering simple model interpretation. Our code is freely
available at https://github.com/claying/GCKN.

14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph