  Graph representation learning is gaining popularity in a wide range of
applications, such as social networks analysis, computational biology, and
recommender systems. However, different with positive results from many
academic studies, applying graph neural networks (GNNs) in a real-world
application is still challenging due to non-stationary environments. The
underlying distribution of streaming data changes unexpectedly, resulting in
different graph structures (a.k.a., concept drift). Therefore, it is essential
to devise a robust graph learning technique so that the model does not overfit
to the training graphs. In this work, we present Hop Sampling, a
straightforward regularization method that can effectively prevent GNNs from
overfishing. The hop sampling randomly selects the number of propagation steps
rather than fixing it, and by doing so, it encourages the model to learn
meaningful node representation for all intermediate propagation layers and to
experience a variety of plausible graphs that are not in the training set.
Particularly, we describe the use case of our method in recommender systems, a
representative example of the real-world non-stationary case. We evaluated hop
sampling on a large-scale real-world LINE dataset and conducted an online A/B/n
test in LINE Coupon recommender systems of LINE Wallet Tab. Experimental
results demonstrate that the proposed scheme improves the prediction accuracy
of GNNs. We observed hop sampling provides 7.97% and 16.93% improvements for
NDCG and MAP compared to non-regularized GNN models in our online service.
Furthermore, models using hop sampling alleviate the oversmoothing issue in
GNNs enabling a deeper model as well as more diversified representation.
