Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Graph Edit Distance by Graph Neural Networks   The emergence of geometric deep learning as a novel framework to deal with
graph-based representations has faded away traditional approaches in favor of
completely new methodologies. In this paper, we propose a new framework able to
combine the advances on deep metric learning with traditional approximations of
the graph edit distance. Hence, we propose an efficient graph distance based on
the novel field of geometric deep learning. Our method employs a message
passing neural network to capture the graph structure, and thus, leveraging
this information for its use on a distance computation. The performance of the
proposed graph distance is validated on two different scenarios. On the one
hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing
its superior performance when compared with (approximate) graph edit distance
benchmarks. On the other hand, demonstrating competitive results for graph
similarity learning when compared with the current state-of-the-art on a recent
benchmark dataset.

28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci