Learning Representation On Graph

"Learning Representation on Graph" is a technique that maps graph-structured data to a low-dimensional vector space, aiming to preserve the topological structure and node attribute information of the graph. Its core objective is to enhance the performance of graph data in machine learning tasks by learning representations of nodes, edges, or subgraphs. This method has significant application value in social network analysis, recommendation systems, bioinformatics, and other fields, effectively handling complex relationships and non-Euclidean data structures.