  Cold start is an essential and persistent problem in recommender systems.
State-of-the-art solutions rely on training hybrid models for both cold-start
and existing users/items, based on the auxiliary information. Such a hybrid
model would compromise the performance of existing users/items, which might
make these solutions not applicable in real-worlds recommender systems where
the experience of existing users/items must be guaranteed. Meanwhile, graph
neural networks (GNNs) have been demonstrated to perform effectively warm
(non-cold-start) recommendations. However, they have never been applied to
handle the cold-start problem in a user-item bipartite graph. This is a
challenging but rewarding task since cold-start users/items do not have links.
Besides, it is nontrivial to design an appropriate GNN to conduct cold-start
recommendations while maintaining the performance for existing users/items. To
bridge the gap, we propose a tailored GNN-based framework (GPatch) that
contains two separate but correlated components. First, an efficient GNN
architecture -- GWarmer, is designed to model the warm users/items. Second, we
construct correlated Patching Networks to simulate and patch GWarmer by
conducting cold-start recommendations. Experiments on benchmark and large-scale
commercial datasets demonstrate that GPatch is significantly superior in
providing recommendations for both existing and cold-start users/items.
