Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Autoencoders with Deconvolutional Networks   Recent studies have indicated that Graph Convolutional Networks (GCNs) act as
a \emph{low pass} filter in spectral domain and encode smoothed node
representations. In this paper, we consider their opposite, namely Graph
Deconvolutional Networks (GDNs) that reconstruct graph signals from smoothed
node representations. We motivate the design of Graph Deconvolutional Networks
via a combination of inverse filters in spectral domain and de-noising layers
in wavelet domain, as the inverse operation results in a \emph{high pass}
filter and may amplify the noise. Based on the proposed GDN, we further propose
a graph autoencoder framework that first encodes smoothed graph representations
with GCN and then decodes accurate graph signals with GDN. We demonstrate the
effectiveness of the proposed method on several tasks including unsupervised
graph-level representation , social recommendation and graph generation

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The categories of this paper are: nlin
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math