Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalization bounds for graph convolutional neural networks via
  Rademacher complexity   This paper aims at studying the sample complexity of graph convolutional
networks (GCNs), by providing tight upper bounds of Rademacher complexity for
GCN models with a single hidden layer. Under regularity conditions, theses
derived complexity bounds explicitly depend on the largest eigenvalue of graph
convolution filter and the degree distribution of the graph. Again, we provide
a lower bound of Rademacher complexity for GCNs to show optimality of our
derived upper bounds. Taking two commonly used examples as representatives, we
discuss the implications of our results in designing graph convolution filters
an graph distribution.

19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg