Funnel graph neural networks with multi-granularity cascaded fusing for protein-protein interaction prediction

The identification of potential protein-protein interactions (PPIs) between humans and viruses is crucial for comprehending viral infection and disease mechanisms at the molecular level. Recently, graph neural networks (GNNs) have emerged as a promising approach to expedite PPI identification. However, GNNs often suffer from over-smoothing when capturing high-order neighbor information. To tackle this issue and effectively capture implicit collaborative information from multi-hop neighbors, we propose FGNN, Funnel Graph Neural Networks with Multi-Granularity Cascaded Fusing, facilitating the distillation of information in a funnel-like manner. Specifically, it enables the mapping of information flow from the full graph to subgraphs and ultimately to nodes. By regarding subgraphs as bridges connecting higher-order neighbors, we ensure the projection of multi-hop neighbors into the same subspace, thereby achieving a comprehensive mapping of the full graph into subgraphs. Moreover, we employ an encoder equipped with a multi-head attention mechanism to effectively map subgraphs onto nodes, facilitating further refinement and compression of information derived from high-order neighbors. FGNN can effectively capture high-order neighbor information whilst relieving over-smoothing. Extensive experiments demonstrate FGNN is superior to the state-of-the-art methods in terms of AUC value. The achieved improvements in the four cardiovascular disease datasets are 7.96 %, 2.3 %, 2.49 %, and 0.82 %, respectively.