Graph Domain

Graph domain research focuses on developing and applying machine learning models, particularly graph neural networks (GNNs), to analyze and learn from data represented as graphs. Current research emphasizes improving GNN transferability across diverse graph domains, addressing challenges like feature and structural heterogeneity through techniques such as low-rank adaptation and spectral alignment, and exploring the potential of graph foundation models and prompt learning paradigms. This field is significant because it enables the analysis of complex relational data in various applications, from social networks and recommendation systems to drug discovery and materials science, and is driving advancements in both theoretical understanding of GNNs and practical model development.