  Performing analytical tasks over graph data has become increasingly
interesting due to the ubiquity and large availability of relational
information. However, unlike images or sentences, there is no notion of
sequence in networks. Nodes (and edges) follow no absolute order, and it is
hard for traditional machine learning (ML) algorithms to recognize a pattern
and generalize their predictions on this type of data. Graph Neural Networks
(GNN) successfully tackled this problem. They became popular after the
generalization of the convolution concept to the graph domain. However, they
possess a large number of hyperparameters and their design and optimization is
currently hand-made, based on heuristics or empirical intuition. Neural
Architecture Search (NAS) methods appear as an interesting solution to this
problem. In this direction, this paper compares two NAS methods for optimizing
GNN: one based on reinforcement learning and a second based on evolutionary
algorithms. Results consider 7 datasets over two search spaces and show that
both methods obtain similar accuracies to a random search, raising the question
of how many of the search space dimensions are actually relevant to the
problem.
