  The recent work ``Combinatorial Optimization with Physics-Inspired Graph
Neural Networks'' [Nat Mach Intell 4 (2022) 367] introduces a physics-inspired
unsupervised Graph Neural Network (GNN) to solve combinatorial optimization
problems on sparse graphs. To test the performances of these GNNs, the authors
of the work show numerical results for two fundamental problems: maximum cut
and maximum independent set (MIS). They conclude that "the graph neural network
optimizer performs on par or outperforms existing solvers, with the ability to
scale beyond the state of the art to problems with millions of variables."
  In this comment, we show that a simple greedy algorithm, running in almost
linear time, can find solutions for the MIS problem of much better quality than
the GNN. The greedy algorithm is faster by a factor of $10^4$ with respect to
the GNN for problems with a million variables. We do not see any good reason
for solving the MIS with these GNN, as well as for using a sledgehammer to
crack nuts.
  In general, many claims of superiority of neural networks in solving
combinatorial problems are at risk of being not solid enough, since we lack
standard benchmarks based on really hard problems. We propose one of such hard
benchmarks, and we hope to see future neural network optimizers tested on these
problems before any claim of superiority is made.
