  Graph neural networks (GNNs) have received remarkable success in link
prediction (GNNLP) tasks. Existing efforts first predefine the subgraph for the
whole dataset and then apply GNNs to encode edge representations by leveraging
the neighborhood structure induced by the fixed subgraph. The prominence of
GNNLP methods significantly relies on the adhoc subgraph. Since node
connectivity in real-world graphs is complex, one shared subgraph is limited
for all edges. Thus, the choices of subgraphs should be personalized to
different edges. However, performing personalized subgraph selection is
nontrivial since the potential selection space grows exponentially to the scale
of edges. Besides, the inference edges are not available during training in
link prediction scenarios, so the selection process needs to be inductive. To
bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a
plug-and-play framework to automatically, personally, and inductively identify
optimal subgraphs for different edges when performing GNNLP. PS2 is
instantiated as a bi-level optimization problem that can be efficiently solved
differently. Coupling GNNLP models with PS2, we suggest a brand-new angle
towards GNNLP training: by first identifying the optimal subgraphs for edges;
and then focusing on training the inference model by using the sampled
subgraphs. Comprehensive experiments endorse the effectiveness of our proposed
method across various GNNLP backbones (GCN, GraphSage, NGCF, LightGCN, and
SEAL) and diverse benchmarks (Planetoid, OGB, and Recommendation datasets). Our
code is publicly available at \url{https://github.com/qiaoyu-tan/PS2}
