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.

蛋白质相互作用（PPI）在广泛的生物过程中起着关键作用。本文回顾了各种基于图的方法，并讨论了它们在 PPI 预测中的应用。我们将这些方法分为两个主要组，一类采用图神经网络（GNN）或图卷积网络（GCN），而第二类则利用图注意力网络（GAT）、图自编码器和 Graph-BERT。我们强调每种方法在处理 PPI 网络中的图结构数据方面的独特方法，并预测在此领域的未来研究方向。