  Graph Neural Networks (GNNs) have advanced the field of machine learning by
utilizing graph-structured data, which is ubiquitous in the real world. GNNs
have applications in various fields, ranging from social network analysis to
drug discovery. GNN training is strenuous, requiring significant computational
resources and human expertise. It makes a trained GNN an indispensable
Intellectual Property (IP) for its owner. Recent studies have shown GNNs to be
vulnerable to model-stealing attacks, which raises concerns over IP rights
protection. Watermarking has been shown to be effective at protecting the IP of
a GNN model. Existing efforts to develop a watermarking scheme for GNNs have
only focused on the node classification and the graph classification tasks.
  To the best of our knowledge, we introduce the first-ever watermarking scheme
for GNNs tailored to the Link Prediction (LP) task. We call our proposed
watermarking scheme GENIE (watermarking Graph nEural Networks for lInk
prEdiction). We design GENIE using a novel backdoor attack to create a trigger
set for two key methods of LP: (1) node representation-based and (2)
subgraph-based. In GENIE, the watermark is embedded into the GNN model by
training it on both the trigger set and a modified training set, resulting in a
watermarked GNN model. To assess a suspect model, we verify the watermark
against the trigger set. We extensively evaluate GENIE across 3 model
architectures (i.e., SEAL, GCN, and GraphSAGE) and 7 real-world datasets.
Furthermore, we validate the robustness of GENIE against 11 state-of-the-art
watermark removal techniques and 3 model extraction attacks. We also
demonstrate that GENIE is robust against ownership piracy attack. Our ownership
demonstration scheme statistically guarantees both False Positive Rate (FPR)
and False Negative Rate (FNR) to be less than $10^{-6}$.
