Role Embedding

Role Embedding is a technique that transforms the role information of nodes in a graph into low-dimensional vector representations, aiming to capture the functional and positional characteristics of the nodes within the graph structure. By learning the role embeddings of nodes, it can effectively enhance the representation ability of graph data and improve the performance of graph analysis tasks such as node classification, link prediction, and community detection. In the fields of complex network analysis and graph machine learning, the application value of Role Embedding is significant, as it helps models better understand and utilize the structural information of graphs.