Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Spectral Multigraph Networks for Discovering and Fusing Relationships in
  Molecules   Spectral Graph Convolutional Networks (GCNs) are a generalization of
convolutional networks to learning on graph-structured data. Applications of
spectral GCNs have been successful, but limited to a few problems where the
graph is fixed, such as shape correspondence and node classification. In this
work, we address this limitation by revisiting a particular family of spectral
graph networks, Chebyshev GCNs, showing its efficacy in solving graph
classification tasks with a variable graph structure and size. Chebyshev GCNs
restrict graphs to have at most one edge between any pair of nodes. To this
end, we propose a novel multigraph network that learns from multi-relational
graphs. We model learned edges with abstract meaning and experiment with
different ways to fuse the representations extracted from annotated and learned
edges, achieving competitive results on a variety of chemical classification
benchmarks.

8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
12
The categories of this paper are: nucl-th