Global-Local Influence Maximization Subgraph Sampling-Based Graph Representation Learning for Innate Immune Response Classification

An understanding of the human innate immune re-sponse has the potential to accelerate the development and clinical trials of drugs and antibiotics. This includes an understanding of T-cell responses, peptides, and the intricate interactions with Human Leukocyte Antigens (HLA). Graph based models capture the structural aspects of these interactions efficiently and graph based neural networks are the best choice of tools to analyse these datasets. However, the polymorphic nature of peptides, coupled with various influencing factors, results in the representation of HLA-peptide interactions as large complex graphs. Traditional graph-based neural networks often face challenges in processing large graphs and suffer due to their high learning and training time with reduced generalization capabilities. To address these challenges, this paper proposes an influence maximization sub-graph sampling classification approach to retain the structural information of large complex molecular graphs. This is achieved using global-local influence maximization (GLIM) that combines Page Rank with Eigenvector centrality. This unique combination enables local substructure connectivity, considering edge weights, which are critical in molecular contexts. The graph neural network (GNN) classification model achieved 0.82% accuracy on the main graphs and 0.83% accuracy on the subgraphs. More notably, the model consumed approximately 81% less memory and was about 72% faster per epoch for the subgraphs compared to the main graphs. The experimental results demonstrate the effectiveness of the subgraphs as they minimize memory resource utilization and processing time, making them a practical and efficient choice for HLA-peptide immunogenetic behavior analysis.