  Graph neural network (GNN) is a deep model for graph representation learning.
One advantage of graph neural network is its ability to incorporate node
features into the learning process. However, this prevents graph neural network
from being applied into featureless graphs. In this paper, we first analyze the
effects of node features on the performance of graph neural network. We show
that GNNs work well if there is a strong correlation between node features and
node labels. Based on these results, we propose new feature initialization
methods that allows to apply graph neural network to non-attributed graphs. Our
experimental results show that the artificial features are highly competitive
with real features.
