  Graph Neural Networks (GNNs) have been widely applied on a variety of
real-world applications, such as social recommendation. However, existing
GNN-based models on social recommendation suffer from serious problems of
generalization and oversmoothness, because of the underexplored negative
sampling method and the direct implanting of the off-the-shelf GNN models. In
this paper, we propose a succinct multi-network GNN-based neural model (NeMo)
for social recommendation. Compared with the existing methods, the proposed
model explores a generative negative sampling strategy, and leverages both the
positive and negative user-item interactions for users' interest propagation.
The experiments show that NeMo outperforms the state-of-the-art baselines on
various real-world benchmark datasets (e.g., by up to 38.8% in terms of
NDCG@15).
