Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Neural Networks are Dynamic Programmers   Recent advances in neural algorithmic reasoning with graph neural networks
(GNNs) are propped up by the notion of algorithmic alignment. Broadly, a neural
network will be better at learning to execute a reasoning task (in terms of
sample complexity) if its individual components align well with the target
algorithm. Specifically, GNNs are claimed to align with dynamic programming
(DP), a general problem-solving strategy which expresses many polynomial-time
algorithms. However, has this alignment truly been demonstrated and
theoretically quantified? Here we show, using methods from category theory and
abstract algebra, that there exists an intricate connection between GNNs and
DP, going well beyond the initial observations over individual algorithms such
as Bellman-Ford. Exposing this connection, we easily verify several prior
findings in the literature, and hope it will serve as a foundation for building
stronger algorithmically aligned GNNs.

25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys