Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Subgraph Matching   Subgraph matching is the problem of determining the presence and location(s)
of a given query graph in a large target graph. Despite being an NP-complete
problem, the subgraph matching problem is crucial in domains ranging from
network science and database systems to biochemistry and cognitive science.
However, existing techniques based on combinatorial matching and integer
programming cannot handle matching problems with both large target and query
graphs. Here we propose NeuroMatch, an accurate, efficient, and robust neural
approach to subgraph matching. NeuroMatch decomposes query and target graphs
into small subgraphs and embeds them using graph neural networks. Trained to
capture geometric constraints corresponding to subgraph relations, NeuroMatch
then efficiently performs subgraph matching directly in the embedding space.
Experiments demonstrate NeuroMatch is 100x faster than existing combinatorial
approaches and 18% more accurate than existing approximate subgraph matching
methods.

9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an