HGMP: Revolutionizing Complex Graph Analysis with Prompt Learning
In the era dominated by language models and machine learning, the importance of structured data is growing rapidly: social networks, biological relationships, and business connections. This data is represented in the form of graphs, which are often not homogeneous: they contain nodes of different types (e.g., people, products, companies) and different types of edges (e.g., “purchased”, “recommended”, “works at”). Processing such heterogeneous graphs requires specialized methods. What are heterogeneous graphs? A heterogeneous graph is a structure in which: ...