  Protein-protein interactions (PPIs) play key roles in a broad range of
biological processes. Numerous strategies have been proposed for predicting
PPIs, and among them, graph-based methods have demonstrated promising outcomes
owing to the inherent graph structure of PPI networks. This paper reviews
various graph-based methodologies, and discusses their applications in PPI
prediction. We classify these approaches into two primary groups based on their
model structures. The first category employs Graph Neural Networks (GNN) or
Graph Convolutional Networks (GCN), while the second category utilizes Graph
Attention Networks (GAT), Graph Auto-Encoders and Graph-BERT. We highlight the
distinctive methodologies of each approach in managing the graph-structured
data inherent in PPI networks and anticipate future research directions in this
domain.
